PFF Consent Date Ref - MethylFlash Global DNAm Analysis
Gillian Goobie
05/05/2022
1 Importing Datasets
1.1 Total PM2.5 Files
Here I am importing the file which contains global DNAm estimates by ELISA-based MethylFlash that are linked to the patient ID.
outfile1 <- here("PFF_Results/PFF_MethylFlash_ExptID_1-751_Combined_2022_05_05.xlsx")
dnam <- read_excel(outfile1, sheet="CalculatedValues")Match up PM25 data with methylation data
outfile2 <- here("PM25_Data/Final_PFF_fILD_PM_BaselineData_2022_02_12.xlsx")
PM <- read_excel(outfile2)1.2 Keep only required columns
str(dnam)## tibble [751 × 12] (S3: tbl_df/tbl/data.frame)
## $ SSID : chr [1:751] "11R4536" "04R1356" "10R4092" "12R5016" ...
## $ sampID : num [1:751] 79012226 79011626 79090026 79014826 79013526 ...
## $ exptID : num [1:751] 1 2 3 4 5 6 7 8 9 10 ...
## $ run_date : POSIXct[1:751], format: "2022-04-06" "2022-04-06" ...
## $ plate : num [1:751] 1 1 1 1 1 1 1 1 1 1 ...
## $ well : chr [1:751] "G2" "A3" "C3" "E3" ...
## $ raw_mean : num [1:751] 0.1109 0.0711 0.1041 0.0822 0.0923 ...
## $ raw_stdev : num [1:751] 0.00545 0.0017 0.01407 0.00325 0.01103 ...
## $ coef_var : num [1:751] 0.0491 0.0239 0.1352 0.0396 0.1195 ...
## $ calc_slope : num [1:751] 0.0935 0.0935 0.0935 0.0935 0.0935 ...
## $ calc_intercept: num [1:751] 0.335 0.335 0.335 0.335 0.335 ...
## $ pct_5mC : num [1:751] 0.0905 0.0592 0.0842 0.0666 0.0742 ...dnam <- dnam %>% dplyr::select(SSID, sampID, exptID, run_date, plate, pct_5mC)1.3 Join Columns
dnam <- left_join(dnam, PM, by="SSID")2 Add Original PM2.5 Matching Files with All Months
outfile2b <- here("PM25_Data/PFF_fILD_2000_2018_PM25_2021_10_08.xlsx")
PM <- read_excel(outfile2b)
PM <- PM %>% rename("SSID"="ID")dnam <- inner_join(PM, dnam, by="SSID")Reorder so “ID” is the first column
dnam <- dnam %>% dplyr::select(ID, everything(.))
dnam <- dnam %>% dplyr::select(!c(PM_date, value))2.1 Pivoting to Long Format
First we need to convert the PM dataframe into the long rather than the wide format, which will allow us to use it more easily in R’s tidyverse as this is “tidy” formatting.
dnam <- dnam %>%
pivot_longer(cols=c(7:234), names_to="PM_date", names_prefix="PM25_")2.2 Convert date PM_date to same format as above
dnamx <- dnam
dnamx$PM_date <- gsub("jan", "01-01-20", dnamx$PM_date)
dnamx$PM_date <- gsub("feb", "01-02-20", dnamx$PM_date)
dnamx$PM_date <- gsub("mar", "01-03-20", dnamx$PM_date)
dnamx$PM_date <- gsub("apr", "01-04-20", dnamx$PM_date)
dnamx$PM_date <- gsub("may", "01-05-20", dnamx$PM_date)
dnamx$PM_date <- gsub("jun", "01-06-20", dnamx$PM_date)
dnamx$PM_date <- gsub("jul", "01-07-20", dnamx$PM_date)
dnamx$PM_date <- gsub("aug", "01-08-20", dnamx$PM_date)
dnamx$PM_date <- gsub("sep", "01-09-20", dnamx$PM_date)
dnamx$PM_date <- gsub("oct", "01-10-20", dnamx$PM_date)
dnamx$PM_date <- gsub("nov", "01-11-20", dnamx$PM_date)
dnamx$PM_date <- gsub("dec", "01-12-20", dnamx$PM_date)
dnamx$PM_date <- format(as.Date(dnamx$PM_date, format="%d-%m-%Y"),"%Y-%m-%d")
dnamx$PM_date <- as.Date(dnamx$PM_date)
dnam <- dnamx2.3 Convert Date Columns to Date Format
Next I need to convert all date columns to proper format
dnam <- dnam %>%
mutate_at(c("run_date", "dx_date", "consent_date", "death_date", "tx_date", "sample_date", "fvc_date", "dlco_date", "censor_date", "deathORtx_date", "DeathTxCensor_date", "PM_date"), as.Date)
str(dnam)## tibble [171,228 × 63] (S3: tbl_df/tbl/data.frame)
## $ ID : num [1:171228] 513 513 513 513 513 513 513 513 513 513 ...
## $ nrow.x : num [1:171228] 27492333 27492333 27492333 27492333 27492333 ...
## $ dist.x : num [1:171228] 0.00707 0.00707 0.00707 0.00707 0.00707 ...
## $ SSID : chr [1:171228] "02R0456" "02R0456" "02R0456" "02R0456" ...
## $ lon : num [1:171228] -123 -123 -123 -123 -123 ...
## $ lat : num [1:171228] 38.4 38.4 38.4 38.4 38.4 ...
## $ sampID : num [1:171228] 77900524 77900524 77900524 77900524 77900524 ...
## $ exptID : num [1:171228] 682 682 682 682 682 682 682 682 682 682 ...
## $ run_date : Date[1:171228], format: "2022-04-25" "2022-04-25" ...
## $ plate : num [1:171228] 17 17 17 17 17 17 17 17 17 17 ...
## $ pct_5mC : num [1:171228] 0.105 0.105 0.105 0.105 0.105 ...
## $ sex : chr [1:171228] "Male" "Male" "Male" "Male" ...
## $ race : chr [1:171228] "W" "W" "W" "W" ...
## $ dich_Race : chr [1:171228] "White" "White" "White" "White" ...
## $ ethnicity : chr [1:171228] "N" "N" "N" "N" ...
## $ smokeHx : chr [1:171228] "Ever" "Ever" "Ever" "Ever" ...
## $ age_dx : num [1:171228] 74 74 74 74 74 ...
## $ status : chr [1:171228] "1" "1" "1" "1" ...
## $ deadORtx : num [1:171228] 1 1 1 1 1 1 1 1 1 1 ...
## $ dx : chr [1:171228] "IPF" "IPF" "IPF" "IPF" ...
## $ dx_group : chr [1:171228] "IPF" "IPF" "IPF" "IPF" ...
## $ dx_date : Date[1:171228], format: "2016-05-20" "2016-05-20" ...
## $ consent_date : Date[1:171228], format: "2016-05-19" "2016-05-19" ...
## $ censor_date : Date[1:171228], format: "2016-06-11" "2016-06-11" ...
## $ tx_date : Date[1:171228], format: NA NA ...
## $ death_date : Date[1:171228], format: "2016-06-11" "2016-06-11" ...
## $ deathORtx_date : Date[1:171228], format: "2016-06-11" "2016-06-11" ...
## $ DeathTxCensor_date: Date[1:171228], format: "2016-06-11" "2016-06-11" ...
## $ sample_date : Date[1:171228], format: "2016-05-19" "2016-05-19" ...
## $ fvc_date : Date[1:171228], format: "2016-03-17" "2016-03-17" ...
## $ dlco_date : Date[1:171228], format: "2016-03-17" "2016-03-17" ...
## $ fvc_pct : num [1:171228] 58.5 58.5 58.5 58.5 58.5 ...
## $ dlco_pct : num [1:171228] 28.8 28.8 28.8 28.8 28.8 ...
## $ fvc_timefromdx : num [1:171228] -0.175 -0.175 -0.175 -0.175 -0.175 ...
## $ dlco_timefromdx : num [1:171228] -0.175 -0.175 -0.175 -0.175 -0.175 ...
## $ Reason_Termination: chr [1:171228] "Death" "Death" "Death" "Death" ...
## $ Death_ILD_Related : chr [1:171228] "Yes" "Yes" "Yes" "Yes" ...
## $ same_zip : logi [1:171228] TRUE TRUE TRUE TRUE TRUE TRUE ...
## $ zip : chr [1:171228] "95405" "95405" "95405" "95405" ...
## $ zip_new : chr [1:171228] "95405" "95405" "95405" "95405" ...
## $ lat.x : num [1:171228] 38.4 38.4 38.4 38.4 38.4 ...
## $ lon.x : num [1:171228] -123 -123 -123 -123 -123 ...
## $ state : chr [1:171228] "CA" "CA" "CA" "CA" ...
## $ major_city : chr [1:171228] "Santa Rosa" "Santa Rosa" "Santa Rosa" "Santa Rosa" ...
## $ time_censoring : num [1:171228] 0.063 0.063 0.063 0.063 0.063 ...
## $ time_death : num [1:171228] 0.063 0.063 0.063 0.063 0.063 ...
## $ time_tx : num [1:171228] NA NA NA NA NA NA NA NA NA NA ...
## $ time_deathORtx : num [1:171228] 0.063 0.063 0.063 0.063 0.063 ...
## $ time_DeathTxCensor: num [1:171228] 0.063 0.063 0.063 0.063 0.063 ...
## $ nrow.y : num [1:171228] 27492333 27492333 27492333 27492333 27492333 ...
## $ dist.y : num [1:171228] 0.00707 0.00707 0.00707 0.00707 0.00707 ...
## $ lon.y : num [1:171228] -123 -123 -123 -123 -123 ...
## $ lat.y : num [1:171228] 38.4 38.4 38.4 38.4 38.4 ...
## $ PM_5yrPreCensor : num [1:171228] 7.62 7.62 7.62 7.62 7.62 ...
## $ PM_5yrPreDx : num [1:171228] 7.62 7.62 7.62 7.62 7.62 ...
## $ PM5yrCensor_dich : chr [1:171228] "Low" "Low" "Low" "Low" ...
## $ PM5yr_dich : chr [1:171228] "Low" "Low" "Low" "Low" ...
## $ dx_IPF : chr [1:171228] "IPF" "IPF" "IPF" "IPF" ...
## $ ruca : num [1:171228] 1 1 1 1 1 1 1 1 1 1 ...
## $ metro : chr [1:171228] "metropolitan" "metropolitan" "metropolitan" "metropolitan" ...
## $ site : chr [1:171228] "02R" "02R" "02R" "02R" ...
## $ PM_date : Date[1:171228], format: "2000-01-01" "2000-02-01" ...
## $ value : num [1:171228] 13 8 6.2 6.5 4.7 ...2.4 Remove unnecessary columns
dnam <- dnam %>% dplyr::select(!c("nrow.x", "dist.x", "lon", "lat", "lat.x", "lon.x", "nrow.y", "dist.y", "lon.y", "lat.y"))2.5 Creating PM2.5 Exposure Variables
Now I’m creating new variables where I am matching up PM averages per year to years of major events for patients (year of diagnosis, year of death/lung transplant/censoring, etc)
2.5.1 5yrs Pre-Sampling
Here I am calculating the average PM value in the 5yrs prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(PM_5yrPreSamp = mean(value[ymd(PM_date)>=(ymd(sample_date) - years(5)) &
ymd(PM_date)<=ymd(sample_date)]))2.5.2 1yr Pre-Sampling
Here I am calculating the average PM value in the 1yr prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(PM_1yrPreSamp = mean(value[ymd(PM_date)>=(ymd(sample_date) - years(1)) &
ymd(PM_date)<=ymd(sample_date)]))2.5.3 6mo Pre-Sampling
Here I am calculating the average PM value in the 6mo prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(PM_6moPreSamp = mean(value[ymd(PM_date)>=(ymd(sample_date) - months(6)) &
ymd(PM_date)<=ymd(sample_date)]))2.5.4 3mo Pre-Sampling
Here I am calculating the average PM value in the 3mo prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(PM_3moPreSamp = mean(value[ymd(PM_date)>=(ymd(sample_date) - months(3)) &
ymd(PM_date)<=ymd(sample_date)]))2.5.5 1mo Pre-Sampling
Here I am calculating the average PM value in the 1mo prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(PM_1moPreSamp = mean(value[ymd(PM_date)>=(ymd(sample_date) - months(2)) &
ymd(PM_date)<=ymd(sample_date)]))Of note, the 1mo pre-sampling with this method actually provides the average PM2.5 value for the month of sampling and the 1mo prior to sampling because the sampling could have occurred at the beginning or end of a month.
2.5.6 Remove Dataframes not in use
rm(list=c("dnamx"))2.5.7 Getting Rid of Duplicated IDs
Now that we have calculated the patient-specific PM exposures, I can get rid of all rows other than the first row for each patient.
dnam <- dnam %>%
distinct_at(vars(ID), .keep_all=T)This takes us down to 734 observations.
2.5.8 Get rid of non-specific “value” column
dnam <- dnam %>% dplyr::select(!value)2.5.9 Remove UPitt patients
dnam <- dnam %>% filter(!str_detect(SSID, "^08R"))This takes us down to 733 observations after we remove UPitt patients which may be doubled up in our Simmons analyses.
3 Add PM2.5 Constituent Files
3.1 SO4 add Original SO4 Matching Files with All Months
outfile3 <- here("PM25_Data/PFF_fILD_2000_2017_SO4_2021_11_05.xlsx")
SO4 <- read_excel(outfile3)dnam <- inner_join(SO4, dnam, by="ID")Reorder so “ID” is the first column
dnam <- dnam %>% dplyr::select(ID, everything(.))3.1.1 Pivoting to Long Format
First we need to convert the PM dataframe into the long rather than the wide format, which will allow us to use it more easily in R’s tidyverse as this is “tidy” formatting.
dnam <- dnam %>%
pivot_longer(cols=c(6:221), names_to="SO4_date", names_prefix="SO4_", names_repair = "minimal")3.1.2 Convert date PM_date to same format as above
dnamx <- dnam
dnamx$SO4_date <- gsub("jan", "01-01-20", dnamx$SO4_date)
dnamx$SO4_date <- gsub("feb", "01-02-20", dnamx$SO4_date)
dnamx$SO4_date <- gsub("mar", "01-03-20", dnamx$SO4_date)
dnamx$SO4_date <- gsub("apr", "01-04-20", dnamx$SO4_date)
dnamx$SO4_date <- gsub("may", "01-05-20", dnamx$SO4_date)
dnamx$SO4_date <- gsub("jun", "01-06-20", dnamx$SO4_date)
dnamx$SO4_date <- gsub("jul", "01-07-20", dnamx$SO4_date)
dnamx$SO4_date <- gsub("aug", "01-08-20", dnamx$SO4_date)
dnamx$SO4_date <- gsub("sep", "01-09-20", dnamx$SO4_date)
dnamx$SO4_date <- gsub("oct", "01-10-20", dnamx$SO4_date)
dnamx$SO4_date <- gsub("nov", "01-11-20", dnamx$SO4_date)
dnamx$SO4_date <- gsub("dec", "01-12-20", dnamx$SO4_date)
dnamx$SO4_date <- format(as.Date(dnamx$SO4_date, format="%d-%m-%Y"),"%Y-%m-%d")
dnamx$SO4_date <- as.Date(dnamx$SO4_date)
dnam <- dnamx4 Convert Date Columns to Date Format
Next I need to convert all date columns to proper format
dnam <- dnam %>%
mutate_at(c("SO4_date"), as.Date)
str(dnam)## tibble [161,136 × 63] (S3: tbl_df/tbl/data.frame)
## $ ID : num [1:161136] 513 513 513 513 513 513 513 513 513 513 ...
## $ nrow : num [1:161136] 27492333 27492333 27492333 27492333 27492333 ...
## $ dist : num [1:161136] 0.00707 0.00707 0.00707 0.00707 0.00707 ...
## $ lon : num [1:161136] -123 -123 -123 -123 -123 ...
## $ lat : num [1:161136] 38.4 38.4 38.4 38.4 38.4 ...
## $ SSID : chr [1:161136] "02R0456" "02R0456" "02R0456" "02R0456" ...
## $ sampID : num [1:161136] 77900524 77900524 77900524 77900524 77900524 ...
## $ exptID : num [1:161136] 682 682 682 682 682 682 682 682 682 682 ...
## $ run_date : Date[1:161136], format: "2022-04-25" "2022-04-25" ...
## $ plate : num [1:161136] 17 17 17 17 17 17 17 17 17 17 ...
## $ pct_5mC : num [1:161136] 0.105 0.105 0.105 0.105 0.105 ...
## $ sex : chr [1:161136] "Male" "Male" "Male" "Male" ...
## $ race : chr [1:161136] "W" "W" "W" "W" ...
## $ dich_Race : chr [1:161136] "White" "White" "White" "White" ...
## $ ethnicity : chr [1:161136] "N" "N" "N" "N" ...
## $ smokeHx : chr [1:161136] "Ever" "Ever" "Ever" "Ever" ...
## $ age_dx : num [1:161136] 74 74 74 74 74 ...
## $ status : chr [1:161136] "1" "1" "1" "1" ...
## $ deadORtx : num [1:161136] 1 1 1 1 1 1 1 1 1 1 ...
## $ dx : chr [1:161136] "IPF" "IPF" "IPF" "IPF" ...
## $ dx_group : chr [1:161136] "IPF" "IPF" "IPF" "IPF" ...
## $ dx_date : Date[1:161136], format: "2016-05-20" "2016-05-20" ...
## $ consent_date : Date[1:161136], format: "2016-05-19" "2016-05-19" ...
## $ censor_date : Date[1:161136], format: "2016-06-11" "2016-06-11" ...
## $ tx_date : Date[1:161136], format: NA NA ...
## $ death_date : Date[1:161136], format: "2016-06-11" "2016-06-11" ...
## $ deathORtx_date : Date[1:161136], format: "2016-06-11" "2016-06-11" ...
## $ DeathTxCensor_date: Date[1:161136], format: "2016-06-11" "2016-06-11" ...
## $ sample_date : Date[1:161136], format: "2016-05-19" "2016-05-19" ...
## $ fvc_date : Date[1:161136], format: "2016-03-17" "2016-03-17" ...
## $ dlco_date : Date[1:161136], format: "2016-03-17" "2016-03-17" ...
## $ fvc_pct : num [1:161136] 58.5 58.5 58.5 58.5 58.5 ...
## $ dlco_pct : num [1:161136] 28.8 28.8 28.8 28.8 28.8 ...
## $ fvc_timefromdx : num [1:161136] -0.175 -0.175 -0.175 -0.175 -0.175 ...
## $ dlco_timefromdx : num [1:161136] -0.175 -0.175 -0.175 -0.175 -0.175 ...
## $ Reason_Termination: chr [1:161136] "Death" "Death" "Death" "Death" ...
## $ Death_ILD_Related : chr [1:161136] "Yes" "Yes" "Yes" "Yes" ...
## $ same_zip : logi [1:161136] TRUE TRUE TRUE TRUE TRUE TRUE ...
## $ zip : chr [1:161136] "95405" "95405" "95405" "95405" ...
## $ zip_new : chr [1:161136] "95405" "95405" "95405" "95405" ...
## $ state : chr [1:161136] "CA" "CA" "CA" "CA" ...
## $ major_city : chr [1:161136] "Santa Rosa" "Santa Rosa" "Santa Rosa" "Santa Rosa" ...
## $ time_censoring : num [1:161136] 0.063 0.063 0.063 0.063 0.063 ...
## $ time_death : num [1:161136] 0.063 0.063 0.063 0.063 0.063 ...
## $ time_tx : num [1:161136] NA NA NA NA NA NA NA NA NA NA ...
## $ time_deathORtx : num [1:161136] 0.063 0.063 0.063 0.063 0.063 ...
## $ time_DeathTxCensor: num [1:161136] 0.063 0.063 0.063 0.063 0.063 ...
## $ PM_5yrPreCensor : num [1:161136] 7.62 7.62 7.62 7.62 7.62 ...
## $ PM_5yrPreDx : num [1:161136] 7.62 7.62 7.62 7.62 7.62 ...
## $ PM5yrCensor_dich : chr [1:161136] "Low" "Low" "Low" "Low" ...
## $ PM5yr_dich : chr [1:161136] "Low" "Low" "Low" "Low" ...
## $ dx_IPF : chr [1:161136] "IPF" "IPF" "IPF" "IPF" ...
## $ ruca : num [1:161136] 1 1 1 1 1 1 1 1 1 1 ...
## $ metro : chr [1:161136] "metropolitan" "metropolitan" "metropolitan" "metropolitan" ...
## $ site : chr [1:161136] "02R" "02R" "02R" "02R" ...
## $ PM_date : Date[1:161136], format: "2000-01-01" "2000-01-01" ...
## $ PM_5yrPreSamp : num [1:161136] 7.62 7.62 7.62 7.62 7.62 ...
## $ PM_1yrPreSamp : num [1:161136] 6.31 6.31 6.31 6.31 6.31 ...
## $ PM_6moPreSamp : num [1:161136] 6.6 6.6 6.6 6.6 6.6 ...
## $ PM_3moPreSamp : num [1:161136] 5.63 5.63 5.63 5.63 5.63 ...
## $ PM_1moPreSamp : num [1:161136] 6.35 6.35 6.35 6.35 6.35 ...
## $ SO4_date : Date[1:161136], format: "2000-01-01" "2000-02-01" ...
## $ value : num [1:161136] 1.3 0.3 0.5 0.6 0.9 ...4.1 Creating SO4 Exposure Variables
Now I’m creating new variables where I am matching up PM averages per year to years of major events for patients (year of diagnosis, year of death/lung transplant/censoring, etc)
4.1.1 5yrs Pre-Sampling
Here I am calculating the average SO4 value in the 5yrs prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(SO4_5yrPreSamp = mean(value[ymd(SO4_date)>=(ymd(sample_date) - years(5)) &
ymd(SO4_date)<=ymd(sample_date)]))4.1.2 1yr Pre-Sampling
Here I am calculating the average SO4 value in the 1yr prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(SO4_1yrPreSamp = mean(value[ymd(SO4_date)>=(ymd(sample_date) - years(1)) &
ymd(SO4_date)<=ymd(sample_date)]))4.1.3 6mo Pre-Sampling
Here I am calculating the average SO4 value in the 6mo prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(SO4_6moPreSamp = mean(value[ymd(SO4_date)>=(ymd(sample_date) - months(6)) &
ymd(SO4_date)<=ymd(sample_date)]))4.1.4 3mo Pre-Sampling
Here I am calculating the average SO4 value in the 3mo prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(SO4_3moPreSamp = mean(value[ymd(SO4_date)>=(ymd(sample_date) - months(3)) &
ymd(SO4_date)<=ymd(sample_date)]))4.1.5 1mo Pre-Sampling
Here I am calculating the average SO4 value in the 1mo prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(SO4_1moPreSamp = mean(value[ymd(SO4_date)>=(ymd(sample_date) - months(2)) &
ymd(SO4_date)<=ymd(sample_date)]))Of note, the 1mo pre-sampling with this method actually provides the average PM2.5 value for the month of sampling and the 1mo prior to sampling because the sampling could have occurred at the beginning or end of a month.
4.1.6 Remove Dataframes and variables not in use
rm(list=c("dnamx", "PM", "SO4"))4.1.7 Getting Rid of Duplicated IDs
Now that we have calculated the patient-specific PM exposures, I can get rid of all rows other than the first row for each patient.
dnam <- dnam %>%
distinct_at(vars(ID), .keep_all=T)This takes us down to 733 observations.
4.1.8 Getting rid of unnecessary columns
dnam <- dnam %>% dplyr::select(!c(nrow, dist, lon, lat, SO4_date, value))4.2 NO3 add Original NO3 Matching Files with All Months
outfile3 <- here("PM25_Data/PFF_fILD_2000_2017_NO3_2021_11_05.xlsx")
NO3 <- read_excel(outfile3)dnam <- inner_join(NO3, dnam, by="ID")Reorder so “ID” is the first column
dnam <- dnam %>% dplyr::select(ID, everything(.))4.2.1 Pivoting to Long Format
First we need to convert the PM dataframe into the long rather than the wide format, which will allow us to use it more easily in R’s tidyverse as this is “tidy” formatting.
dnam <- dnam %>%
pivot_longer(cols=c(6:221), names_to="NO3_date", names_prefix="NIT_", names_repair = "minimal")4.2.2 Convert date PM_date to same format as above
dnamx <- dnam
dnamx$NO3_date <- gsub("jan", "01-01-20", dnamx$NO3_date)
dnamx$NO3_date <- gsub("feb", "01-02-20", dnamx$NO3_date)
dnamx$NO3_date <- gsub("mar", "01-03-20", dnamx$NO3_date)
dnamx$NO3_date <- gsub("apr", "01-04-20", dnamx$NO3_date)
dnamx$NO3_date <- gsub("may", "01-05-20", dnamx$NO3_date)
dnamx$NO3_date <- gsub("jun", "01-06-20", dnamx$NO3_date)
dnamx$NO3_date <- gsub("jul", "01-07-20", dnamx$NO3_date)
dnamx$NO3_date <- gsub("aug", "01-08-20", dnamx$NO3_date)
dnamx$NO3_date <- gsub("sep", "01-09-20", dnamx$NO3_date)
dnamx$NO3_date <- gsub("oct", "01-10-20", dnamx$NO3_date)
dnamx$NO3_date <- gsub("nov", "01-11-20", dnamx$NO3_date)
dnamx$NO3_date <- gsub("dec", "01-12-20", dnamx$NO3_date)
dnamx$NO3_date <- format(as.Date(dnamx$NO3_date, format="%d-%m-%Y"),"%Y-%m-%d")
dnamx$NO3_date <- as.Date(dnamx$NO3_date)
dnam <- dnamx5 Convert Date Columns to Date Format
Next I need to convert all date columns to proper format
dnam <- dnam %>%
mutate_at(c("NO3_date"), as.Date)
str(dnam)## tibble [161,136 × 68] (S3: tbl_df/tbl/data.frame)
## $ ID : num [1:161136] 513 513 513 513 513 513 513 513 513 513 ...
## $ nrow : num [1:161136] 27492333 27492333 27492333 27492333 27492333 ...
## $ dist : num [1:161136] 0.00707 0.00707 0.00707 0.00707 0.00707 ...
## $ lon : num [1:161136] -123 -123 -123 -123 -123 ...
## $ lat : num [1:161136] 38.4 38.4 38.4 38.4 38.4 ...
## $ SSID : chr [1:161136] "02R0456" "02R0456" "02R0456" "02R0456" ...
## $ sampID : num [1:161136] 77900524 77900524 77900524 77900524 77900524 ...
## $ exptID : num [1:161136] 682 682 682 682 682 682 682 682 682 682 ...
## $ run_date : Date[1:161136], format: "2022-04-25" "2022-04-25" ...
## $ plate : num [1:161136] 17 17 17 17 17 17 17 17 17 17 ...
## $ pct_5mC : num [1:161136] 0.105 0.105 0.105 0.105 0.105 ...
## $ sex : chr [1:161136] "Male" "Male" "Male" "Male" ...
## $ race : chr [1:161136] "W" "W" "W" "W" ...
## $ dich_Race : chr [1:161136] "White" "White" "White" "White" ...
## $ ethnicity : chr [1:161136] "N" "N" "N" "N" ...
## $ smokeHx : chr [1:161136] "Ever" "Ever" "Ever" "Ever" ...
## $ age_dx : num [1:161136] 74 74 74 74 74 ...
## $ status : chr [1:161136] "1" "1" "1" "1" ...
## $ deadORtx : num [1:161136] 1 1 1 1 1 1 1 1 1 1 ...
## $ dx : chr [1:161136] "IPF" "IPF" "IPF" "IPF" ...
## $ dx_group : chr [1:161136] "IPF" "IPF" "IPF" "IPF" ...
## $ dx_date : Date[1:161136], format: "2016-05-20" "2016-05-20" ...
## $ consent_date : Date[1:161136], format: "2016-05-19" "2016-05-19" ...
## $ censor_date : Date[1:161136], format: "2016-06-11" "2016-06-11" ...
## $ tx_date : Date[1:161136], format: NA NA ...
## $ death_date : Date[1:161136], format: "2016-06-11" "2016-06-11" ...
## $ deathORtx_date : Date[1:161136], format: "2016-06-11" "2016-06-11" ...
## $ DeathTxCensor_date: Date[1:161136], format: "2016-06-11" "2016-06-11" ...
## $ sample_date : Date[1:161136], format: "2016-05-19" "2016-05-19" ...
## $ fvc_date : Date[1:161136], format: "2016-03-17" "2016-03-17" ...
## $ dlco_date : Date[1:161136], format: "2016-03-17" "2016-03-17" ...
## $ fvc_pct : num [1:161136] 58.5 58.5 58.5 58.5 58.5 ...
## $ dlco_pct : num [1:161136] 28.8 28.8 28.8 28.8 28.8 ...
## $ fvc_timefromdx : num [1:161136] -0.175 -0.175 -0.175 -0.175 -0.175 ...
## $ dlco_timefromdx : num [1:161136] -0.175 -0.175 -0.175 -0.175 -0.175 ...
## $ Reason_Termination: chr [1:161136] "Death" "Death" "Death" "Death" ...
## $ Death_ILD_Related : chr [1:161136] "Yes" "Yes" "Yes" "Yes" ...
## $ same_zip : logi [1:161136] TRUE TRUE TRUE TRUE TRUE TRUE ...
## $ zip : chr [1:161136] "95405" "95405" "95405" "95405" ...
## $ zip_new : chr [1:161136] "95405" "95405" "95405" "95405" ...
## $ state : chr [1:161136] "CA" "CA" "CA" "CA" ...
## $ major_city : chr [1:161136] "Santa Rosa" "Santa Rosa" "Santa Rosa" "Santa Rosa" ...
## $ time_censoring : num [1:161136] 0.063 0.063 0.063 0.063 0.063 ...
## $ time_death : num [1:161136] 0.063 0.063 0.063 0.063 0.063 ...
## $ time_tx : num [1:161136] NA NA NA NA NA NA NA NA NA NA ...
## $ time_deathORtx : num [1:161136] 0.063 0.063 0.063 0.063 0.063 ...
## $ time_DeathTxCensor: num [1:161136] 0.063 0.063 0.063 0.063 0.063 ...
## $ PM_5yrPreCensor : num [1:161136] 7.62 7.62 7.62 7.62 7.62 ...
## $ PM_5yrPreDx : num [1:161136] 7.62 7.62 7.62 7.62 7.62 ...
## $ PM5yrCensor_dich : chr [1:161136] "Low" "Low" "Low" "Low" ...
## $ PM5yr_dich : chr [1:161136] "Low" "Low" "Low" "Low" ...
## $ dx_IPF : chr [1:161136] "IPF" "IPF" "IPF" "IPF" ...
## $ ruca : num [1:161136] 1 1 1 1 1 1 1 1 1 1 ...
## $ metro : chr [1:161136] "metropolitan" "metropolitan" "metropolitan" "metropolitan" ...
## $ site : chr [1:161136] "02R" "02R" "02R" "02R" ...
## $ PM_date : Date[1:161136], format: "2000-01-01" "2000-01-01" ...
## $ PM_5yrPreSamp : num [1:161136] 7.62 7.62 7.62 7.62 7.62 ...
## $ PM_1yrPreSamp : num [1:161136] 6.31 6.31 6.31 6.31 6.31 ...
## $ PM_6moPreSamp : num [1:161136] 6.6 6.6 6.6 6.6 6.6 ...
## $ PM_3moPreSamp : num [1:161136] 5.63 5.63 5.63 5.63 5.63 ...
## $ PM_1moPreSamp : num [1:161136] 6.35 6.35 6.35 6.35 6.35 ...
## $ SO4_5yrPreSamp : num [1:161136] 0.673 0.673 0.673 0.673 0.673 ...
## $ SO4_1yrPreSamp : num [1:161136] 0.475 0.475 0.475 0.475 0.475 ...
## $ SO4_6moPreSamp : num [1:161136] 0.417 0.417 0.417 0.417 0.417 ...
## $ SO4_3moPreSamp : num [1:161136] 0.567 0.567 0.567 0.567 0.567 ...
## $ SO4_1moPreSamp : num [1:161136] 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 ...
## $ NO3_date : Date[1:161136], format: "2000-01-01" "2000-02-01" ...
## $ value : num [1:161136] 5 1.2 1.7 0.7 0.3 ...5.1 Creating NO3 Exposure Variables
Now I’m creating new variables where I am matching up PM averages per year to years of major events for patients (year of diagnosis, year of death/lung transplant/censoring, etc)
5.1.1 5yrs Pre-Sampling
Here I am calculating the average NO3 value in the 5yrs prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(NO3_5yrPreSamp = mean(value[ymd(NO3_date)>=(ymd(sample_date) - years(5)) &
ymd(NO3_date)<=ymd(sample_date)]))5.1.2 1yr Pre-Sampling
Here I am calculating the average NO3 value in the 1yr prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(NO3_1yrPreSamp = mean(value[ymd(NO3_date)>=(ymd(sample_date) - years(1)) &
ymd(NO3_date)<=ymd(sample_date)]))5.1.3 6mo Pre-Sampling
Here I am calculating the average NO3 value in the 6mo prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(NO3_6moPreSamp = mean(value[ymd(NO3_date)>=(ymd(sample_date) - months(6)) &
ymd(NO3_date)<=ymd(sample_date)]))5.1.4 3mo Pre-Sampling
Here I am calculating the average NO3 value in the 3mo prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(NO3_3moPreSamp = mean(value[ymd(NO3_date)>=(ymd(sample_date) - months(3)) &
ymd(NO3_date)<=ymd(sample_date)]))5.1.5 1mo Pre-Sampling
Here I am calculating the average NO3 value in the 1mo prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(NO3_1moPreSamp = mean(value[ymd(NO3_date)>=(ymd(sample_date) - months(2)) &
ymd(NO3_date)<=ymd(sample_date)]))Of note, the 1mo pre-sampling with this method actually provides the average PM2.5 value for the month of sampling and the 1mo prior to sampling because the sampling could have occurred at the beginning or end of a month.
5.1.6 Remove Dataframes and variables not in use
rm(list=c("dnamx", "NO3"))
dnam <- dnam %>% dplyr::select(!c(nrow, dist, lon, lat, value, NO3_date))5.1.7 Getting Rid of Duplicated IDs
Now that we have calculated the patient-specific PM exposures, I can get rid of all rows other than the first row for each patient.
dnam <- dnam %>%
distinct_at(vars(ID), .keep_all=T)This takes us down to 733 observations.
5.2 NH4 add Original NH4 Matching Files with All Months
outfile3 <- here("PM25_Data/PFF_fILD_2000_2017_NH4_2021_11_05.xlsx")
NH4 <- read_excel(outfile3)dnam <- inner_join(NH4, dnam, by="ID")Reorder so “ID” is the first column
dnam <- dnam %>% dplyr::select(ID, everything(.))5.2.1 Pivoting to Long Format
First we need to convert the PM dataframe into the long rather than the wide format, which will allow us to use it more easily in R’s tidyverse as this is “tidy” formatting.
dnam <- dnam %>%
pivot_longer(cols=c(6:221), names_to="NH4_date", names_prefix="NH4_", names_repair = "minimal")5.2.2 Convert date PM_date to same format as above
dnamx <- dnam
dnamx$NH4_date <- gsub("jan", "01-01-20", dnamx$NH4_date)
dnamx$NH4_date <- gsub("feb", "01-02-20", dnamx$NH4_date)
dnamx$NH4_date <- gsub("mar", "01-03-20", dnamx$NH4_date)
dnamx$NH4_date <- gsub("apr", "01-04-20", dnamx$NH4_date)
dnamx$NH4_date <- gsub("may", "01-05-20", dnamx$NH4_date)
dnamx$NH4_date <- gsub("jun", "01-06-20", dnamx$NH4_date)
dnamx$NH4_date <- gsub("jul", "01-07-20", dnamx$NH4_date)
dnamx$NH4_date <- gsub("aug", "01-08-20", dnamx$NH4_date)
dnamx$NH4_date <- gsub("sep", "01-09-20", dnamx$NH4_date)
dnamx$NH4_date <- gsub("oct", "01-10-20", dnamx$NH4_date)
dnamx$NH4_date <- gsub("nov", "01-11-20", dnamx$NH4_date)
dnamx$NH4_date <- gsub("dec", "01-12-20", dnamx$NH4_date)
dnamx$NH4_date <- format(as.Date(dnamx$NH4_date, format="%d-%m-%Y"),"%Y-%m-%d")
dnamx$NH4_date <- as.Date(dnamx$NH4_date)
dnam <- dnamx6 Convert Date Columns to Date Format
Next I need to convert all date columns to proper format
dnam <- dnam %>%
mutate_at(c("NH4_date"), as.Date)
str(dnam)## tibble [161,136 × 73] (S3: tbl_df/tbl/data.frame)
## $ ID : num [1:161136] 513 513 513 513 513 513 513 513 513 513 ...
## $ nrow : num [1:161136] 27492333 27492333 27492333 27492333 27492333 ...
## $ dist : num [1:161136] 0.00707 0.00707 0.00707 0.00707 0.00707 ...
## $ lon : num [1:161136] -123 -123 -123 -123 -123 ...
## $ lat : num [1:161136] 38.4 38.4 38.4 38.4 38.4 ...
## $ SSID : chr [1:161136] "02R0456" "02R0456" "02R0456" "02R0456" ...
## $ sampID : num [1:161136] 77900524 77900524 77900524 77900524 77900524 ...
## $ exptID : num [1:161136] 682 682 682 682 682 682 682 682 682 682 ...
## $ run_date : Date[1:161136], format: "2022-04-25" "2022-04-25" ...
## $ plate : num [1:161136] 17 17 17 17 17 17 17 17 17 17 ...
## $ pct_5mC : num [1:161136] 0.105 0.105 0.105 0.105 0.105 ...
## $ sex : chr [1:161136] "Male" "Male" "Male" "Male" ...
## $ race : chr [1:161136] "W" "W" "W" "W" ...
## $ dich_Race : chr [1:161136] "White" "White" "White" "White" ...
## $ ethnicity : chr [1:161136] "N" "N" "N" "N" ...
## $ smokeHx : chr [1:161136] "Ever" "Ever" "Ever" "Ever" ...
## $ age_dx : num [1:161136] 74 74 74 74 74 ...
## $ status : chr [1:161136] "1" "1" "1" "1" ...
## $ deadORtx : num [1:161136] 1 1 1 1 1 1 1 1 1 1 ...
## $ dx : chr [1:161136] "IPF" "IPF" "IPF" "IPF" ...
## $ dx_group : chr [1:161136] "IPF" "IPF" "IPF" "IPF" ...
## $ dx_date : Date[1:161136], format: "2016-05-20" "2016-05-20" ...
## $ consent_date : Date[1:161136], format: "2016-05-19" "2016-05-19" ...
## $ censor_date : Date[1:161136], format: "2016-06-11" "2016-06-11" ...
## $ tx_date : Date[1:161136], format: NA NA ...
## $ death_date : Date[1:161136], format: "2016-06-11" "2016-06-11" ...
## $ deathORtx_date : Date[1:161136], format: "2016-06-11" "2016-06-11" ...
## $ DeathTxCensor_date: Date[1:161136], format: "2016-06-11" "2016-06-11" ...
## $ sample_date : Date[1:161136], format: "2016-05-19" "2016-05-19" ...
## $ fvc_date : Date[1:161136], format: "2016-03-17" "2016-03-17" ...
## $ dlco_date : Date[1:161136], format: "2016-03-17" "2016-03-17" ...
## $ fvc_pct : num [1:161136] 58.5 58.5 58.5 58.5 58.5 ...
## $ dlco_pct : num [1:161136] 28.8 28.8 28.8 28.8 28.8 ...
## $ fvc_timefromdx : num [1:161136] -0.175 -0.175 -0.175 -0.175 -0.175 ...
## $ dlco_timefromdx : num [1:161136] -0.175 -0.175 -0.175 -0.175 -0.175 ...
## $ Reason_Termination: chr [1:161136] "Death" "Death" "Death" "Death" ...
## $ Death_ILD_Related : chr [1:161136] "Yes" "Yes" "Yes" "Yes" ...
## $ same_zip : logi [1:161136] TRUE TRUE TRUE TRUE TRUE TRUE ...
## $ zip : chr [1:161136] "95405" "95405" "95405" "95405" ...
## $ zip_new : chr [1:161136] "95405" "95405" "95405" "95405" ...
## $ state : chr [1:161136] "CA" "CA" "CA" "CA" ...
## $ major_city : chr [1:161136] "Santa Rosa" "Santa Rosa" "Santa Rosa" "Santa Rosa" ...
## $ time_censoring : num [1:161136] 0.063 0.063 0.063 0.063 0.063 ...
## $ time_death : num [1:161136] 0.063 0.063 0.063 0.063 0.063 ...
## $ time_tx : num [1:161136] NA NA NA NA NA NA NA NA NA NA ...
## $ time_deathORtx : num [1:161136] 0.063 0.063 0.063 0.063 0.063 ...
## $ time_DeathTxCensor: num [1:161136] 0.063 0.063 0.063 0.063 0.063 ...
## $ PM_5yrPreCensor : num [1:161136] 7.62 7.62 7.62 7.62 7.62 ...
## $ PM_5yrPreDx : num [1:161136] 7.62 7.62 7.62 7.62 7.62 ...
## $ PM5yrCensor_dich : chr [1:161136] "Low" "Low" "Low" "Low" ...
## $ PM5yr_dich : chr [1:161136] "Low" "Low" "Low" "Low" ...
## $ dx_IPF : chr [1:161136] "IPF" "IPF" "IPF" "IPF" ...
## $ ruca : num [1:161136] 1 1 1 1 1 1 1 1 1 1 ...
## $ metro : chr [1:161136] "metropolitan" "metropolitan" "metropolitan" "metropolitan" ...
## $ site : chr [1:161136] "02R" "02R" "02R" "02R" ...
## $ PM_date : Date[1:161136], format: "2000-01-01" "2000-01-01" ...
## $ PM_5yrPreSamp : num [1:161136] 7.62 7.62 7.62 7.62 7.62 ...
## $ PM_1yrPreSamp : num [1:161136] 6.31 6.31 6.31 6.31 6.31 ...
## $ PM_6moPreSamp : num [1:161136] 6.6 6.6 6.6 6.6 6.6 ...
## $ PM_3moPreSamp : num [1:161136] 5.63 5.63 5.63 5.63 5.63 ...
## $ PM_1moPreSamp : num [1:161136] 6.35 6.35 6.35 6.35 6.35 ...
## $ SO4_5yrPreSamp : num [1:161136] 0.673 0.673 0.673 0.673 0.673 ...
## $ SO4_1yrPreSamp : num [1:161136] 0.475 0.475 0.475 0.475 0.475 ...
## $ SO4_6moPreSamp : num [1:161136] 0.417 0.417 0.417 0.417 0.417 ...
## $ SO4_3moPreSamp : num [1:161136] 0.567 0.567 0.567 0.567 0.567 ...
## $ SO4_1moPreSamp : num [1:161136] 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 ...
## $ NO3_5yrPreSamp : num [1:161136] 0.923 0.923 0.923 0.923 0.923 ...
## $ NO3_1yrPreSamp : num [1:161136] 0.8 0.8 0.8 0.8 0.8 ...
## $ NO3_6moPreSamp : num [1:161136] 0.883 0.883 0.883 0.883 0.883 ...
## $ NO3_3moPreSamp : num [1:161136] 0.533 0.533 0.533 0.533 0.533 ...
## $ NO3_1moPreSamp : num [1:161136] 0.6 0.6 0.6 0.6 0.6 ...
## $ NH4_date : Date[1:161136], format: "2000-01-01" "2000-02-01" ...
## $ value : num [1:161136] 0.4 0 0.1 0.1 0.1 ...6.1 Creating NH4 Exposure Variables
Now I’m creating new variables where I am matching up PM averages per year to years of major events for patients (year of diagnosis, year of death/lung transplant/censoring, etc)
6.1.1 5yrs Pre-Sampling
Here I am calculating the average NH4 value in the 5yrs prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(NH4_5yrPreSamp = mean(value[ymd(NH4_date)>=(ymd(sample_date) - years(5)) &
ymd(NH4_date)<=ymd(sample_date)]))6.1.2 1yr Pre-Sampling
Here I am calculating the average NH4 value in the 1yr prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(NH4_1yrPreSamp = mean(value[ymd(NH4_date)>=(ymd(sample_date) - years(1)) &
ymd(NH4_date)<=ymd(sample_date)]))6.1.3 6mo Pre-Sampling
Here I am calculating the average NH4 value in the 6mo prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(NH4_6moPreSamp = mean(value[ymd(NH4_date)>=(ymd(sample_date) - months(6)) &
ymd(NH4_date)<=ymd(sample_date)]))6.1.4 3mo Pre-Sampling
Here I am calculating the average NH4 value in the 3mo prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(NH4_3moPreSamp = mean(value[ymd(NH4_date)>=(ymd(sample_date) - months(3)) &
ymd(NH4_date)<=ymd(sample_date)]))6.1.5 1mo Pre-Sampling
Here I am calculating the average NH4 value in the 1mo prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(NH4_1moPreSamp = mean(value[ymd(NH4_date)>=(ymd(sample_date) - months(2)) &
ymd(NH4_date)<=ymd(sample_date)]))Of note, the 1mo pre-sampling with this method actually provides the average PM2.5 value for the month of sampling and the 1mo prior to sampling because the sampling could have occurred at the beginning or end of a month.
6.1.6 Remove Dataframes and variables not in use
rm(list=c("dnamx", "NH4"))
dnam <- dnam %>% dplyr::select(!c(nrow, dist, lon, lat, value, NH4_date))6.1.7 Getting Rid of Duplicated IDs
Now that we have calculated the patient-specific PM exposures, I can get rid of all rows other than the first row for each patient.
dnam <- dnam %>%
distinct_at(vars(ID), .keep_all=T)This takes us down to 733 observations.
6.2 BC add Original BC Matching Files with All Months
outfile3 <- here("PM25_Data/PFF_fILD_2000_2017_BC_2021_11_05.xlsx")
BC <- read_excel(outfile3)dnam <- inner_join(BC, dnam, by="ID")Reorder so “ID” is the first column
dnam <- dnam %>% dplyr::select(ID, everything(.))6.2.1 Pivoting to Long Format
First we need to convert the PM dataframe into the long rather than the wide format, which will allow us to use it more easily in R’s tidyverse as this is “tidy” formatting.
dnam <- dnam %>%
pivot_longer(cols=c(6:221), names_to="BC_date", names_prefix="BC_", names_repair = "minimal")6.2.2 Convert date PM_date to same format as above
dnamx <- dnam
dnamx$BC_date <- gsub("jan", "01-01-20", dnamx$BC_date)
dnamx$BC_date <- gsub("feb", "01-02-20", dnamx$BC_date)
dnamx$BC_date <- gsub("mar", "01-03-20", dnamx$BC_date)
dnamx$BC_date <- gsub("apr", "01-04-20", dnamx$BC_date)
dnamx$BC_date <- gsub("may", "01-05-20", dnamx$BC_date)
dnamx$BC_date <- gsub("jun", "01-06-20", dnamx$BC_date)
dnamx$BC_date <- gsub("jul", "01-07-20", dnamx$BC_date)
dnamx$BC_date <- gsub("aug", "01-08-20", dnamx$BC_date)
dnamx$BC_date <- gsub("sep", "01-09-20", dnamx$BC_date)
dnamx$BC_date <- gsub("oct", "01-10-20", dnamx$BC_date)
dnamx$BC_date <- gsub("nov", "01-11-20", dnamx$BC_date)
dnamx$BC_date <- gsub("dec", "01-12-20", dnamx$BC_date)
dnamx$BC_date <- format(as.Date(dnamx$BC_date, format="%d-%m-%Y"),"%Y-%m-%d")
dnamx$BC_date <- as.Date(dnamx$BC_date)
dnam <- dnamx7 Convert Date Columns to Date Format
Next I need to convert all date columns to proper format
dnam <- dnam %>%
mutate_at(c("BC_date"), as.Date)
str(dnam)## tibble [161,136 × 78] (S3: tbl_df/tbl/data.frame)
## $ ID : num [1:161136] 513 513 513 513 513 513 513 513 513 513 ...
## $ nrow : num [1:161136] 27492333 27492333 27492333 27492333 27492333 ...
## $ dist : num [1:161136] 0.00707 0.00707 0.00707 0.00707 0.00707 ...
## $ lon : num [1:161136] -123 -123 -123 -123 -123 ...
## $ lat : num [1:161136] 38.4 38.4 38.4 38.4 38.4 ...
## $ SSID : chr [1:161136] "02R0456" "02R0456" "02R0456" "02R0456" ...
## $ sampID : num [1:161136] 77900524 77900524 77900524 77900524 77900524 ...
## $ exptID : num [1:161136] 682 682 682 682 682 682 682 682 682 682 ...
## $ run_date : Date[1:161136], format: "2022-04-25" "2022-04-25" ...
## $ plate : num [1:161136] 17 17 17 17 17 17 17 17 17 17 ...
## $ pct_5mC : num [1:161136] 0.105 0.105 0.105 0.105 0.105 ...
## $ sex : chr [1:161136] "Male" "Male" "Male" "Male" ...
## $ race : chr [1:161136] "W" "W" "W" "W" ...
## $ dich_Race : chr [1:161136] "White" "White" "White" "White" ...
## $ ethnicity : chr [1:161136] "N" "N" "N" "N" ...
## $ smokeHx : chr [1:161136] "Ever" "Ever" "Ever" "Ever" ...
## $ age_dx : num [1:161136] 74 74 74 74 74 ...
## $ status : chr [1:161136] "1" "1" "1" "1" ...
## $ deadORtx : num [1:161136] 1 1 1 1 1 1 1 1 1 1 ...
## $ dx : chr [1:161136] "IPF" "IPF" "IPF" "IPF" ...
## $ dx_group : chr [1:161136] "IPF" "IPF" "IPF" "IPF" ...
## $ dx_date : Date[1:161136], format: "2016-05-20" "2016-05-20" ...
## $ consent_date : Date[1:161136], format: "2016-05-19" "2016-05-19" ...
## $ censor_date : Date[1:161136], format: "2016-06-11" "2016-06-11" ...
## $ tx_date : Date[1:161136], format: NA NA ...
## $ death_date : Date[1:161136], format: "2016-06-11" "2016-06-11" ...
## $ deathORtx_date : Date[1:161136], format: "2016-06-11" "2016-06-11" ...
## $ DeathTxCensor_date: Date[1:161136], format: "2016-06-11" "2016-06-11" ...
## $ sample_date : Date[1:161136], format: "2016-05-19" "2016-05-19" ...
## $ fvc_date : Date[1:161136], format: "2016-03-17" "2016-03-17" ...
## $ dlco_date : Date[1:161136], format: "2016-03-17" "2016-03-17" ...
## $ fvc_pct : num [1:161136] 58.5 58.5 58.5 58.5 58.5 ...
## $ dlco_pct : num [1:161136] 28.8 28.8 28.8 28.8 28.8 ...
## $ fvc_timefromdx : num [1:161136] -0.175 -0.175 -0.175 -0.175 -0.175 ...
## $ dlco_timefromdx : num [1:161136] -0.175 -0.175 -0.175 -0.175 -0.175 ...
## $ Reason_Termination: chr [1:161136] "Death" "Death" "Death" "Death" ...
## $ Death_ILD_Related : chr [1:161136] "Yes" "Yes" "Yes" "Yes" ...
## $ same_zip : logi [1:161136] TRUE TRUE TRUE TRUE TRUE TRUE ...
## $ zip : chr [1:161136] "95405" "95405" "95405" "95405" ...
## $ zip_new : chr [1:161136] "95405" "95405" "95405" "95405" ...
## $ state : chr [1:161136] "CA" "CA" "CA" "CA" ...
## $ major_city : chr [1:161136] "Santa Rosa" "Santa Rosa" "Santa Rosa" "Santa Rosa" ...
## $ time_censoring : num [1:161136] 0.063 0.063 0.063 0.063 0.063 ...
## $ time_death : num [1:161136] 0.063 0.063 0.063 0.063 0.063 ...
## $ time_tx : num [1:161136] NA NA NA NA NA NA NA NA NA NA ...
## $ time_deathORtx : num [1:161136] 0.063 0.063 0.063 0.063 0.063 ...
## $ time_DeathTxCensor: num [1:161136] 0.063 0.063 0.063 0.063 0.063 ...
## $ PM_5yrPreCensor : num [1:161136] 7.62 7.62 7.62 7.62 7.62 ...
## $ PM_5yrPreDx : num [1:161136] 7.62 7.62 7.62 7.62 7.62 ...
## $ PM5yrCensor_dich : chr [1:161136] "Low" "Low" "Low" "Low" ...
## $ PM5yr_dich : chr [1:161136] "Low" "Low" "Low" "Low" ...
## $ dx_IPF : chr [1:161136] "IPF" "IPF" "IPF" "IPF" ...
## $ ruca : num [1:161136] 1 1 1 1 1 1 1 1 1 1 ...
## $ metro : chr [1:161136] "metropolitan" "metropolitan" "metropolitan" "metropolitan" ...
## $ site : chr [1:161136] "02R" "02R" "02R" "02R" ...
## $ PM_date : Date[1:161136], format: "2000-01-01" "2000-01-01" ...
## $ PM_5yrPreSamp : num [1:161136] 7.62 7.62 7.62 7.62 7.62 ...
## $ PM_1yrPreSamp : num [1:161136] 6.31 6.31 6.31 6.31 6.31 ...
## $ PM_6moPreSamp : num [1:161136] 6.6 6.6 6.6 6.6 6.6 ...
## $ PM_3moPreSamp : num [1:161136] 5.63 5.63 5.63 5.63 5.63 ...
## $ PM_1moPreSamp : num [1:161136] 6.35 6.35 6.35 6.35 6.35 ...
## $ SO4_5yrPreSamp : num [1:161136] 0.673 0.673 0.673 0.673 0.673 ...
## $ SO4_1yrPreSamp : num [1:161136] 0.475 0.475 0.475 0.475 0.475 ...
## $ SO4_6moPreSamp : num [1:161136] 0.417 0.417 0.417 0.417 0.417 ...
## $ SO4_3moPreSamp : num [1:161136] 0.567 0.567 0.567 0.567 0.567 ...
## $ SO4_1moPreSamp : num [1:161136] 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 ...
## $ NO3_5yrPreSamp : num [1:161136] 0.923 0.923 0.923 0.923 0.923 ...
## $ NO3_1yrPreSamp : num [1:161136] 0.8 0.8 0.8 0.8 0.8 ...
## $ NO3_6moPreSamp : num [1:161136] 0.883 0.883 0.883 0.883 0.883 ...
## $ NO3_3moPreSamp : num [1:161136] 0.533 0.533 0.533 0.533 0.533 ...
## $ NO3_1moPreSamp : num [1:161136] 0.6 0.6 0.6 0.6 0.6 ...
## $ NH4_5yrPreSamp : num [1:161136] 0.143 0.143 0.143 0.143 0.143 ...
## $ NH4_1yrPreSamp : num [1:161136] 0.0583 0.0583 0.0583 0.0583 0.0583 ...
## $ NH4_6moPreSamp : num [1:161136] 0.05 0.05 0.05 0.05 0.05 ...
## $ NH4_3moPreSamp : num [1:161136] 0.0333 0.0333 0.0333 0.0333 0.0333 ...
## $ NH4_1moPreSamp : num [1:161136] 0.05 0.05 0.05 0.05 0.05 ...
## $ BC_date : Date[1:161136], format: "2000-01-01" "2000-02-01" ...
## $ value : num [1:161136] 1.4 1 0.7 1.2 0.9 ...7.1 Creating BC Exposure Variables
Now I’m creating new variables where I am matching up PM averages per year to years of major events for patients (year of diagnosis, year of death/lung transplant/censoring, etc)
7.1.1 5yrs Pre-Sampling
Here I am calculating the average BC value in the 5yrs prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(BC_5yrPreSamp = mean(value[ymd(BC_date)>=(ymd(sample_date) - years(5)) &
ymd(BC_date)<=ymd(sample_date)]))7.1.2 1yr Pre-Sampling
Here I am calculating the average BC value in the 1yr prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(BC_1yrPreSamp = mean(value[ymd(BC_date)>=(ymd(sample_date) - years(1)) &
ymd(BC_date)<=ymd(sample_date)]))7.1.3 6mo Pre-Sampling
Here I am calculating the average BC value in the 6mo prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(BC_6moPreSamp = mean(value[ymd(BC_date)>=(ymd(sample_date) - months(6)) &
ymd(BC_date)<=ymd(sample_date)]))7.1.4 3mo Pre-Sampling
Here I am calculating the average BC value in the 3mo prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(BC_3moPreSamp = mean(value[ymd(BC_date)>=(ymd(sample_date) - months(3)) &
ymd(BC_date)<=ymd(sample_date)]))7.1.5 1mo Pre-Sampling
Here I am calculating the average BC value in the 1mo prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(BC_1moPreSamp = mean(value[ymd(BC_date)>=(ymd(sample_date) - months(2)) &
ymd(BC_date)<=ymd(sample_date)]))Of note, the 1mo pre-sampling with this method actually provides the average PM2.5 value for the month of sampling and the 1mo prior to sampling because the sampling could have occurred at the beginning or end of a month.
7.1.6 Remove Dataframes and variables not in use
rm(list=c("dnamx", "BC"))
dnam <- dnam %>% dplyr::select(!c(nrow, dist, lon, lat, value, BC_date))7.1.7 Getting Rid of Duplicated IDs
Now that we have calculated the patient-specific PM exposures, I can get rid of all rows other than the first row for each patient.
dnam <- dnam %>%
distinct_at(vars(ID), .keep_all=T)This takes us down to 733 observations.
7.2 OM add Original OM Matching Files with All Months
outfile3 <- here("PM25_Data/PFF_fILD_2000_2017_OM_2021_11_05.xlsx")
OM <- read_excel(outfile3)dnam <- inner_join(OM, dnam, by="ID")Reorder so “ID” is the first column
dnam <- dnam %>% dplyr::select(ID, everything(.))7.2.1 Pivoting to Long Format
First we need to convert the PM dataframe into the long rather than the wide format, which will allow us to use it more easily in R’s tidyverse as this is “tidy” formatting.
dnam <- dnam %>%
pivot_longer(cols=c(6:221), names_to="OM_date", names_prefix="OM_", names_repair = "minimal")7.2.2 Convert date PM_date to same format as above
dnamx <- dnam
dnamx$OM_date <- gsub("jan", "01-01-20", dnamx$OM_date)
dnamx$OM_date <- gsub("feb", "01-02-20", dnamx$OM_date)
dnamx$OM_date <- gsub("mar", "01-03-20", dnamx$OM_date)
dnamx$OM_date <- gsub("apr", "01-04-20", dnamx$OM_date)
dnamx$OM_date <- gsub("may", "01-05-20", dnamx$OM_date)
dnamx$OM_date <- gsub("jun", "01-06-20", dnamx$OM_date)
dnamx$OM_date <- gsub("jul", "01-07-20", dnamx$OM_date)
dnamx$OM_date <- gsub("aug", "01-08-20", dnamx$OM_date)
dnamx$OM_date <- gsub("sep", "01-09-20", dnamx$OM_date)
dnamx$OM_date <- gsub("oct", "01-10-20", dnamx$OM_date)
dnamx$OM_date <- gsub("nov", "01-11-20", dnamx$OM_date)
dnamx$OM_date <- gsub("dec", "01-12-20", dnamx$OM_date)
dnamx$OM_date <- format(as.Date(dnamx$OM_date, format="%d-%m-%Y"),"%Y-%m-%d")
dnamx$OM_date <- as.Date(dnamx$OM_date)
dnam <- dnamx8 Convert Date Columns to Date Format
Next I need to convert all date columns to proper format
dnam <- dnam %>%
mutate_at(c("OM_date"), as.Date)
str(dnam)## tibble [161,136 × 83] (S3: tbl_df/tbl/data.frame)
## $ ID : num [1:161136] 513 513 513 513 513 513 513 513 513 513 ...
## $ nrow : num [1:161136] 27492333 27492333 27492333 27492333 27492333 ...
## $ dist : num [1:161136] 0.00707 0.00707 0.00707 0.00707 0.00707 ...
## $ lon : num [1:161136] -123 -123 -123 -123 -123 ...
## $ lat : num [1:161136] 38.4 38.4 38.4 38.4 38.4 ...
## $ SSID : chr [1:161136] "02R0456" "02R0456" "02R0456" "02R0456" ...
## $ sampID : num [1:161136] 77900524 77900524 77900524 77900524 77900524 ...
## $ exptID : num [1:161136] 682 682 682 682 682 682 682 682 682 682 ...
## $ run_date : Date[1:161136], format: "2022-04-25" "2022-04-25" ...
## $ plate : num [1:161136] 17 17 17 17 17 17 17 17 17 17 ...
## $ pct_5mC : num [1:161136] 0.105 0.105 0.105 0.105 0.105 ...
## $ sex : chr [1:161136] "Male" "Male" "Male" "Male" ...
## $ race : chr [1:161136] "W" "W" "W" "W" ...
## $ dich_Race : chr [1:161136] "White" "White" "White" "White" ...
## $ ethnicity : chr [1:161136] "N" "N" "N" "N" ...
## $ smokeHx : chr [1:161136] "Ever" "Ever" "Ever" "Ever" ...
## $ age_dx : num [1:161136] 74 74 74 74 74 ...
## $ status : chr [1:161136] "1" "1" "1" "1" ...
## $ deadORtx : num [1:161136] 1 1 1 1 1 1 1 1 1 1 ...
## $ dx : chr [1:161136] "IPF" "IPF" "IPF" "IPF" ...
## $ dx_group : chr [1:161136] "IPF" "IPF" "IPF" "IPF" ...
## $ dx_date : Date[1:161136], format: "2016-05-20" "2016-05-20" ...
## $ consent_date : Date[1:161136], format: "2016-05-19" "2016-05-19" ...
## $ censor_date : Date[1:161136], format: "2016-06-11" "2016-06-11" ...
## $ tx_date : Date[1:161136], format: NA NA ...
## $ death_date : Date[1:161136], format: "2016-06-11" "2016-06-11" ...
## $ deathORtx_date : Date[1:161136], format: "2016-06-11" "2016-06-11" ...
## $ DeathTxCensor_date: Date[1:161136], format: "2016-06-11" "2016-06-11" ...
## $ sample_date : Date[1:161136], format: "2016-05-19" "2016-05-19" ...
## $ fvc_date : Date[1:161136], format: "2016-03-17" "2016-03-17" ...
## $ dlco_date : Date[1:161136], format: "2016-03-17" "2016-03-17" ...
## $ fvc_pct : num [1:161136] 58.5 58.5 58.5 58.5 58.5 ...
## $ dlco_pct : num [1:161136] 28.8 28.8 28.8 28.8 28.8 ...
## $ fvc_timefromdx : num [1:161136] -0.175 -0.175 -0.175 -0.175 -0.175 ...
## $ dlco_timefromdx : num [1:161136] -0.175 -0.175 -0.175 -0.175 -0.175 ...
## $ Reason_Termination: chr [1:161136] "Death" "Death" "Death" "Death" ...
## $ Death_ILD_Related : chr [1:161136] "Yes" "Yes" "Yes" "Yes" ...
## $ same_zip : logi [1:161136] TRUE TRUE TRUE TRUE TRUE TRUE ...
## $ zip : chr [1:161136] "95405" "95405" "95405" "95405" ...
## $ zip_new : chr [1:161136] "95405" "95405" "95405" "95405" ...
## $ state : chr [1:161136] "CA" "CA" "CA" "CA" ...
## $ major_city : chr [1:161136] "Santa Rosa" "Santa Rosa" "Santa Rosa" "Santa Rosa" ...
## $ time_censoring : num [1:161136] 0.063 0.063 0.063 0.063 0.063 ...
## $ time_death : num [1:161136] 0.063 0.063 0.063 0.063 0.063 ...
## $ time_tx : num [1:161136] NA NA NA NA NA NA NA NA NA NA ...
## $ time_deathORtx : num [1:161136] 0.063 0.063 0.063 0.063 0.063 ...
## $ time_DeathTxCensor: num [1:161136] 0.063 0.063 0.063 0.063 0.063 ...
## $ PM_5yrPreCensor : num [1:161136] 7.62 7.62 7.62 7.62 7.62 ...
## $ PM_5yrPreDx : num [1:161136] 7.62 7.62 7.62 7.62 7.62 ...
## $ PM5yrCensor_dich : chr [1:161136] "Low" "Low" "Low" "Low" ...
## $ PM5yr_dich : chr [1:161136] "Low" "Low" "Low" "Low" ...
## $ dx_IPF : chr [1:161136] "IPF" "IPF" "IPF" "IPF" ...
## $ ruca : num [1:161136] 1 1 1 1 1 1 1 1 1 1 ...
## $ metro : chr [1:161136] "metropolitan" "metropolitan" "metropolitan" "metropolitan" ...
## $ site : chr [1:161136] "02R" "02R" "02R" "02R" ...
## $ PM_date : Date[1:161136], format: "2000-01-01" "2000-01-01" ...
## $ PM_5yrPreSamp : num [1:161136] 7.62 7.62 7.62 7.62 7.62 ...
## $ PM_1yrPreSamp : num [1:161136] 6.31 6.31 6.31 6.31 6.31 ...
## $ PM_6moPreSamp : num [1:161136] 6.6 6.6 6.6 6.6 6.6 ...
## $ PM_3moPreSamp : num [1:161136] 5.63 5.63 5.63 5.63 5.63 ...
## $ PM_1moPreSamp : num [1:161136] 6.35 6.35 6.35 6.35 6.35 ...
## $ SO4_5yrPreSamp : num [1:161136] 0.673 0.673 0.673 0.673 0.673 ...
## $ SO4_1yrPreSamp : num [1:161136] 0.475 0.475 0.475 0.475 0.475 ...
## $ SO4_6moPreSamp : num [1:161136] 0.417 0.417 0.417 0.417 0.417 ...
## $ SO4_3moPreSamp : num [1:161136] 0.567 0.567 0.567 0.567 0.567 ...
## $ SO4_1moPreSamp : num [1:161136] 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 ...
## $ NO3_5yrPreSamp : num [1:161136] 0.923 0.923 0.923 0.923 0.923 ...
## $ NO3_1yrPreSamp : num [1:161136] 0.8 0.8 0.8 0.8 0.8 ...
## $ NO3_6moPreSamp : num [1:161136] 0.883 0.883 0.883 0.883 0.883 ...
## $ NO3_3moPreSamp : num [1:161136] 0.533 0.533 0.533 0.533 0.533 ...
## $ NO3_1moPreSamp : num [1:161136] 0.6 0.6 0.6 0.6 0.6 ...
## $ NH4_5yrPreSamp : num [1:161136] 0.143 0.143 0.143 0.143 0.143 ...
## $ NH4_1yrPreSamp : num [1:161136] 0.0583 0.0583 0.0583 0.0583 0.0583 ...
## $ NH4_6moPreSamp : num [1:161136] 0.05 0.05 0.05 0.05 0.05 ...
## $ NH4_3moPreSamp : num [1:161136] 0.0333 0.0333 0.0333 0.0333 0.0333 ...
## $ NH4_1moPreSamp : num [1:161136] 0.05 0.05 0.05 0.05 0.05 ...
## $ BC_5yrPreSamp : num [1:161136] 0.738 0.738 0.738 0.738 0.738 ...
## $ BC_1yrPreSamp : num [1:161136] 0.575 0.575 0.575 0.575 0.575 ...
## $ BC_6moPreSamp : num [1:161136] 0.667 0.667 0.667 0.667 0.667 ...
## $ BC_3moPreSamp : num [1:161136] 0.367 0.367 0.367 0.367 0.367 ...
## $ BC_1moPreSamp : num [1:161136] 0.35 0.35 0.35 0.35 0.35 ...
## $ OM_date : Date[1:161136], format: "2000-01-01" "2000-02-01" ...
## $ value : num [1:161136] 10.9 3.2 11.3 9.7 5.5 ...8.1 Creating OM Exposure Variables
Now I’m creating new variables where I am matching up PM averages per year to years of major events for patients (year of diagnosis, year of death/lung transplant/censoring, etc)
8.1.1 5yrs Pre-Sampling
Here I am calculating the average OM value in the 5yrs prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(OM_5yrPreSamp = mean(value[ymd(OM_date)>=(ymd(sample_date) - years(5)) &
ymd(OM_date)<=ymd(sample_date)]))8.1.2 1yr Pre-Sampling
Here I am calculating the average OM value in the 1yr prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(OM_1yrPreSamp = mean(value[ymd(OM_date)>=(ymd(sample_date) - years(1)) &
ymd(OM_date)<=ymd(sample_date)]))8.1.3 6mo Pre-Sampling
Here I am calculating the average OM value in the 6mo prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(OM_6moPreSamp = mean(value[ymd(OM_date)>=(ymd(sample_date) - months(6)) &
ymd(OM_date)<=ymd(sample_date)]))8.1.4 3mo Pre-Sampling
Here I am calculating the average OM value in the 3mo prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(OM_3moPreSamp = mean(value[ymd(OM_date)>=(ymd(sample_date) - months(3)) &
ymd(OM_date)<=ymd(sample_date)]))8.1.5 1mo Pre-Sampling
Here I am calculating the average OM value in the 1mo prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(OM_1moPreSamp = mean(value[ymd(OM_date)>=(ymd(sample_date) - months(2)) &
ymd(OM_date)<=ymd(sample_date)]))Of note, the 1mo pre-sampling with this method actually provides the average PM2.5 value for the month of sampling and the 1mo prior to sampling because the sampling could have occurred at the beginning or end of a month.
8.1.6 Remove Dataframes and variables not in use
rm(list=c("dnamx", "OM"))
dnam <- dnam %>% dplyr::select(!c(nrow, dist, lon, lat, value, OM_date))8.1.7 Getting Rid of Duplicated IDs
Now that we have calculated the patient-specific PM exposures, I can get rid of all rows other than the first row for each patient.
dnam <- dnam %>%
distinct_at(vars(ID), .keep_all=T)This takes us down to 733 observations.
8.2 SS add Original SS Matching Files with All Months
outfile3 <- here("PM25_Data/PFF_fILD_2000_2017_SS_2021_11_05.xlsx")
SS <- read_excel(outfile3)dnam <- inner_join(SS, dnam, by="ID")Reorder so “ID” is the first column
dnam <- dnam %>% dplyr::select(ID, everything(.))8.2.1 Pivoting to Long Format
First we need to convert the PM dataframe into the long rather than the wide format, which will allow us to use it more easily in R’s tidyverse as this is “tidy” formatting.
dnam <- dnam %>%
pivot_longer(cols=c(6:221), names_to="SS_date", names_prefix="SS_", names_repair = "minimal")8.2.2 Convert date PM_date to same format as above
dnamx <- dnam
dnamx$SS_date <- gsub("jan", "01-01-20", dnamx$SS_date)
dnamx$SS_date <- gsub("feb", "01-02-20", dnamx$SS_date)
dnamx$SS_date <- gsub("mar", "01-03-20", dnamx$SS_date)
dnamx$SS_date <- gsub("apr", "01-04-20", dnamx$SS_date)
dnamx$SS_date <- gsub("may", "01-05-20", dnamx$SS_date)
dnamx$SS_date <- gsub("jun", "01-06-20", dnamx$SS_date)
dnamx$SS_date <- gsub("jul", "01-07-20", dnamx$SS_date)
dnamx$SS_date <- gsub("aug", "01-08-20", dnamx$SS_date)
dnamx$SS_date <- gsub("sep", "01-09-20", dnamx$SS_date)
dnamx$SS_date <- gsub("oct", "01-10-20", dnamx$SS_date)
dnamx$SS_date <- gsub("nov", "01-11-20", dnamx$SS_date)
dnamx$SS_date <- gsub("dec", "01-12-20", dnamx$SS_date)
dnamx$SS_date <- format(as.Date(dnamx$SS_date, format="%d-%m-%Y"),"%Y-%m-%d")
dnamx$SS_date <- as.Date(dnamx$SS_date)
dnam <- dnamx9 Convert Date Columns to Date Format
Next I need to convert all date columns to proper format
dnam <- dnam %>%
mutate_at(c("SS_date"), as.Date)
str(dnam)## tibble [161,136 × 88] (S3: tbl_df/tbl/data.frame)
## $ ID : num [1:161136] 513 513 513 513 513 513 513 513 513 513 ...
## $ nrow : num [1:161136] 27492333 27492333 27492333 27492333 27492333 ...
## $ dist : num [1:161136] 0.00707 0.00707 0.00707 0.00707 0.00707 ...
## $ lon : num [1:161136] -123 -123 -123 -123 -123 ...
## $ lat : num [1:161136] 38.4 38.4 38.4 38.4 38.4 ...
## $ SSID : chr [1:161136] "02R0456" "02R0456" "02R0456" "02R0456" ...
## $ sampID : num [1:161136] 77900524 77900524 77900524 77900524 77900524 ...
## $ exptID : num [1:161136] 682 682 682 682 682 682 682 682 682 682 ...
## $ run_date : Date[1:161136], format: "2022-04-25" "2022-04-25" ...
## $ plate : num [1:161136] 17 17 17 17 17 17 17 17 17 17 ...
## $ pct_5mC : num [1:161136] 0.105 0.105 0.105 0.105 0.105 ...
## $ sex : chr [1:161136] "Male" "Male" "Male" "Male" ...
## $ race : chr [1:161136] "W" "W" "W" "W" ...
## $ dich_Race : chr [1:161136] "White" "White" "White" "White" ...
## $ ethnicity : chr [1:161136] "N" "N" "N" "N" ...
## $ smokeHx : chr [1:161136] "Ever" "Ever" "Ever" "Ever" ...
## $ age_dx : num [1:161136] 74 74 74 74 74 ...
## $ status : chr [1:161136] "1" "1" "1" "1" ...
## $ deadORtx : num [1:161136] 1 1 1 1 1 1 1 1 1 1 ...
## $ dx : chr [1:161136] "IPF" "IPF" "IPF" "IPF" ...
## $ dx_group : chr [1:161136] "IPF" "IPF" "IPF" "IPF" ...
## $ dx_date : Date[1:161136], format: "2016-05-20" "2016-05-20" ...
## $ consent_date : Date[1:161136], format: "2016-05-19" "2016-05-19" ...
## $ censor_date : Date[1:161136], format: "2016-06-11" "2016-06-11" ...
## $ tx_date : Date[1:161136], format: NA NA ...
## $ death_date : Date[1:161136], format: "2016-06-11" "2016-06-11" ...
## $ deathORtx_date : Date[1:161136], format: "2016-06-11" "2016-06-11" ...
## $ DeathTxCensor_date: Date[1:161136], format: "2016-06-11" "2016-06-11" ...
## $ sample_date : Date[1:161136], format: "2016-05-19" "2016-05-19" ...
## $ fvc_date : Date[1:161136], format: "2016-03-17" "2016-03-17" ...
## $ dlco_date : Date[1:161136], format: "2016-03-17" "2016-03-17" ...
## $ fvc_pct : num [1:161136] 58.5 58.5 58.5 58.5 58.5 ...
## $ dlco_pct : num [1:161136] 28.8 28.8 28.8 28.8 28.8 ...
## $ fvc_timefromdx : num [1:161136] -0.175 -0.175 -0.175 -0.175 -0.175 ...
## $ dlco_timefromdx : num [1:161136] -0.175 -0.175 -0.175 -0.175 -0.175 ...
## $ Reason_Termination: chr [1:161136] "Death" "Death" "Death" "Death" ...
## $ Death_ILD_Related : chr [1:161136] "Yes" "Yes" "Yes" "Yes" ...
## $ same_zip : logi [1:161136] TRUE TRUE TRUE TRUE TRUE TRUE ...
## $ zip : chr [1:161136] "95405" "95405" "95405" "95405" ...
## $ zip_new : chr [1:161136] "95405" "95405" "95405" "95405" ...
## $ state : chr [1:161136] "CA" "CA" "CA" "CA" ...
## $ major_city : chr [1:161136] "Santa Rosa" "Santa Rosa" "Santa Rosa" "Santa Rosa" ...
## $ time_censoring : num [1:161136] 0.063 0.063 0.063 0.063 0.063 ...
## $ time_death : num [1:161136] 0.063 0.063 0.063 0.063 0.063 ...
## $ time_tx : num [1:161136] NA NA NA NA NA NA NA NA NA NA ...
## $ time_deathORtx : num [1:161136] 0.063 0.063 0.063 0.063 0.063 ...
## $ time_DeathTxCensor: num [1:161136] 0.063 0.063 0.063 0.063 0.063 ...
## $ PM_5yrPreCensor : num [1:161136] 7.62 7.62 7.62 7.62 7.62 ...
## $ PM_5yrPreDx : num [1:161136] 7.62 7.62 7.62 7.62 7.62 ...
## $ PM5yrCensor_dich : chr [1:161136] "Low" "Low" "Low" "Low" ...
## $ PM5yr_dich : chr [1:161136] "Low" "Low" "Low" "Low" ...
## $ dx_IPF : chr [1:161136] "IPF" "IPF" "IPF" "IPF" ...
## $ ruca : num [1:161136] 1 1 1 1 1 1 1 1 1 1 ...
## $ metro : chr [1:161136] "metropolitan" "metropolitan" "metropolitan" "metropolitan" ...
## $ site : chr [1:161136] "02R" "02R" "02R" "02R" ...
## $ PM_date : Date[1:161136], format: "2000-01-01" "2000-01-01" ...
## $ PM_5yrPreSamp : num [1:161136] 7.62 7.62 7.62 7.62 7.62 ...
## $ PM_1yrPreSamp : num [1:161136] 6.31 6.31 6.31 6.31 6.31 ...
## $ PM_6moPreSamp : num [1:161136] 6.6 6.6 6.6 6.6 6.6 ...
## $ PM_3moPreSamp : num [1:161136] 5.63 5.63 5.63 5.63 5.63 ...
## $ PM_1moPreSamp : num [1:161136] 6.35 6.35 6.35 6.35 6.35 ...
## $ SO4_5yrPreSamp : num [1:161136] 0.673 0.673 0.673 0.673 0.673 ...
## $ SO4_1yrPreSamp : num [1:161136] 0.475 0.475 0.475 0.475 0.475 ...
## $ SO4_6moPreSamp : num [1:161136] 0.417 0.417 0.417 0.417 0.417 ...
## $ SO4_3moPreSamp : num [1:161136] 0.567 0.567 0.567 0.567 0.567 ...
## $ SO4_1moPreSamp : num [1:161136] 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 ...
## $ NO3_5yrPreSamp : num [1:161136] 0.923 0.923 0.923 0.923 0.923 ...
## $ NO3_1yrPreSamp : num [1:161136] 0.8 0.8 0.8 0.8 0.8 ...
## $ NO3_6moPreSamp : num [1:161136] 0.883 0.883 0.883 0.883 0.883 ...
## $ NO3_3moPreSamp : num [1:161136] 0.533 0.533 0.533 0.533 0.533 ...
## $ NO3_1moPreSamp : num [1:161136] 0.6 0.6 0.6 0.6 0.6 ...
## $ NH4_5yrPreSamp : num [1:161136] 0.143 0.143 0.143 0.143 0.143 ...
## $ NH4_1yrPreSamp : num [1:161136] 0.0583 0.0583 0.0583 0.0583 0.0583 ...
## $ NH4_6moPreSamp : num [1:161136] 0.05 0.05 0.05 0.05 0.05 ...
## $ NH4_3moPreSamp : num [1:161136] 0.0333 0.0333 0.0333 0.0333 0.0333 ...
## $ NH4_1moPreSamp : num [1:161136] 0.05 0.05 0.05 0.05 0.05 ...
## $ BC_5yrPreSamp : num [1:161136] 0.738 0.738 0.738 0.738 0.738 ...
## $ BC_1yrPreSamp : num [1:161136] 0.575 0.575 0.575 0.575 0.575 ...
## $ BC_6moPreSamp : num [1:161136] 0.667 0.667 0.667 0.667 0.667 ...
## $ BC_3moPreSamp : num [1:161136] 0.367 0.367 0.367 0.367 0.367 ...
## $ BC_1moPreSamp : num [1:161136] 0.35 0.35 0.35 0.35 0.35 ...
## $ OM_5yrPreSamp : num [1:161136] 3.95 3.95 3.95 3.95 3.95 ...
## $ OM_1yrPreSamp : num [1:161136] 3.08 3.08 3.08 3.08 3.08 ...
## $ OM_6moPreSamp : num [1:161136] 3.52 3.52 3.52 3.52 3.52 ...
## $ OM_3moPreSamp : num [1:161136] 2.1 2.1 2.1 2.1 2.1 ...
## $ OM_1moPreSamp : num [1:161136] 2.1 2.1 2.1 2.1 2.1 ...
## $ SS_date : Date[1:161136], format: "2000-01-01" "2000-02-01" ...
## $ value : num [1:161136] 1.3 1.4 7.4 0.9 1.5 ...9.1 Creating SS Exposure Variables
Now I’m creating new variables where I am matching up PM averages per year to years of major events for patients (year of diagnosis, year of death/lung transplant/censoring, etc)
9.1.1 5yrs Pre-Sampling
Here I am calculating the average SS value in the 5yrs prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(SS_5yrPreSamp = mean(value[ymd(SS_date)>=(ymd(sample_date) - years(5)) &
ymd(SS_date)<=ymd(sample_date)]))9.1.2 1yr Pre-Sampling
Here I am calculating the average SS value in the 1yr prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(SS_1yrPreSamp = mean(value[ymd(SS_date)>=(ymd(sample_date) - years(1)) &
ymd(SS_date)<=ymd(sample_date)]))9.1.3 6mo Pre-Sampling
Here I am calculating the average SS value in the 6mo prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(SS_6moPreSamp = mean(value[ymd(SS_date)>=(ymd(sample_date) - months(6)) &
ymd(SS_date)<=ymd(sample_date)]))9.1.4 3mo Pre-Sampling
Here I am calculating the average SS value in the 3mo prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(SS_3moPreSamp = mean(value[ymd(SS_date)>=(ymd(sample_date) - months(3)) &
ymd(SS_date)<=ymd(sample_date)]))9.1.5 1mo Pre-Sampling
Here I am calculating the average SS value in the 1mo prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(SS_1moPreSamp = mean(value[ymd(SS_date)>=(ymd(sample_date) - months(2)) &
ymd(SS_date)<=ymd(sample_date)]))Of note, the 1mo pre-sampling with this method actually provides the average PM2.5 value for the month of sampling and the 1mo prior to sampling because the sampling could have occurred at the beginning or end of a month.
9.1.6 Remove Dataframes and variables not in use
rm(list=c("dnamx", "SS"))
dnam <- dnam %>% dplyr::select(!c(nrow, dist, lon, lat, value, SS_date))9.1.7 Getting Rid of Duplicated IDs
Now that we have calculated the patient-specific PM exposures, I can get rid of all rows other than the first row for each patient.
dnam <- dnam %>%
distinct_at(vars(ID), .keep_all=T)This takes us down to 733 observations.
9.2 Soil add Original Soil Matching Files with All Months
outfile3 <- here("PM25_Data/PFF_fILD_2000_2017_soil_2021_11_05.xlsx")
Soil <- read_excel(outfile3)dnam <- inner_join(Soil, dnam, by="ID")Reorder so “ID” is the first column
dnam <- dnam %>% dplyr::select(ID, everything(.))9.2.1 Pivoting to Long Format
First we need to convert the PM dataframe into the long rather than the wide format, which will allow us to use it more easily in R’s tidyverse as this is “tidy” formatting.
dnam <- dnam %>%
pivot_longer(cols=c(6:221), names_to="Soil_date", names_prefix="soil_", names_repair = "minimal")9.2.2 Convert date PM_date to same format as above
dnamx <- dnam
dnamx$Soil_date <- gsub("jan", "01-01-20", dnamx$Soil_date)
dnamx$Soil_date <- gsub("feb", "01-02-20", dnamx$Soil_date)
dnamx$Soil_date <- gsub("mar", "01-03-20", dnamx$Soil_date)
dnamx$Soil_date <- gsub("apr", "01-04-20", dnamx$Soil_date)
dnamx$Soil_date <- gsub("may", "01-05-20", dnamx$Soil_date)
dnamx$Soil_date <- gsub("jun", "01-06-20", dnamx$Soil_date)
dnamx$Soil_date <- gsub("jul", "01-07-20", dnamx$Soil_date)
dnamx$Soil_date <- gsub("aug", "01-08-20", dnamx$Soil_date)
dnamx$Soil_date <- gsub("sep", "01-09-20", dnamx$Soil_date)
dnamx$Soil_date <- gsub("oct", "01-10-20", dnamx$Soil_date)
dnamx$Soil_date <- gsub("nov", "01-11-20", dnamx$Soil_date)
dnamx$Soil_date <- gsub("dec", "01-12-20", dnamx$Soil_date)
dnamx$Soil_date <- format(as.Date(dnamx$Soil_date, format="%d-%m-%Y"),"%Y-%m-%d")
dnamx$Soil_date <- as.Date(dnamx$Soil_date)
dnam <- dnamx10 Convert Date Columns to Date Format
Next I need to convert all date columns to proper format
dnam <- dnam %>%
mutate_at(c("Soil_date"), as.Date)
str(dnam)## tibble [161,136 × 93] (S3: tbl_df/tbl/data.frame)
## $ ID : num [1:161136] 513 513 513 513 513 513 513 513 513 513 ...
## $ nrow : num [1:161136] 27492333 27492333 27492333 27492333 27492333 ...
## $ dist : num [1:161136] 0.00707 0.00707 0.00707 0.00707 0.00707 ...
## $ lon : num [1:161136] -123 -123 -123 -123 -123 ...
## $ lat : num [1:161136] 38.4 38.4 38.4 38.4 38.4 ...
## $ SSID : chr [1:161136] "02R0456" "02R0456" "02R0456" "02R0456" ...
## $ sampID : num [1:161136] 77900524 77900524 77900524 77900524 77900524 ...
## $ exptID : num [1:161136] 682 682 682 682 682 682 682 682 682 682 ...
## $ run_date : Date[1:161136], format: "2022-04-25" "2022-04-25" ...
## $ plate : num [1:161136] 17 17 17 17 17 17 17 17 17 17 ...
## $ pct_5mC : num [1:161136] 0.105 0.105 0.105 0.105 0.105 ...
## $ sex : chr [1:161136] "Male" "Male" "Male" "Male" ...
## $ race : chr [1:161136] "W" "W" "W" "W" ...
## $ dich_Race : chr [1:161136] "White" "White" "White" "White" ...
## $ ethnicity : chr [1:161136] "N" "N" "N" "N" ...
## $ smokeHx : chr [1:161136] "Ever" "Ever" "Ever" "Ever" ...
## $ age_dx : num [1:161136] 74 74 74 74 74 ...
## $ status : chr [1:161136] "1" "1" "1" "1" ...
## $ deadORtx : num [1:161136] 1 1 1 1 1 1 1 1 1 1 ...
## $ dx : chr [1:161136] "IPF" "IPF" "IPF" "IPF" ...
## $ dx_group : chr [1:161136] "IPF" "IPF" "IPF" "IPF" ...
## $ dx_date : Date[1:161136], format: "2016-05-20" "2016-05-20" ...
## $ consent_date : Date[1:161136], format: "2016-05-19" "2016-05-19" ...
## $ censor_date : Date[1:161136], format: "2016-06-11" "2016-06-11" ...
## $ tx_date : Date[1:161136], format: NA NA ...
## $ death_date : Date[1:161136], format: "2016-06-11" "2016-06-11" ...
## $ deathORtx_date : Date[1:161136], format: "2016-06-11" "2016-06-11" ...
## $ DeathTxCensor_date: Date[1:161136], format: "2016-06-11" "2016-06-11" ...
## $ sample_date : Date[1:161136], format: "2016-05-19" "2016-05-19" ...
## $ fvc_date : Date[1:161136], format: "2016-03-17" "2016-03-17" ...
## $ dlco_date : Date[1:161136], format: "2016-03-17" "2016-03-17" ...
## $ fvc_pct : num [1:161136] 58.5 58.5 58.5 58.5 58.5 ...
## $ dlco_pct : num [1:161136] 28.8 28.8 28.8 28.8 28.8 ...
## $ fvc_timefromdx : num [1:161136] -0.175 -0.175 -0.175 -0.175 -0.175 ...
## $ dlco_timefromdx : num [1:161136] -0.175 -0.175 -0.175 -0.175 -0.175 ...
## $ Reason_Termination: chr [1:161136] "Death" "Death" "Death" "Death" ...
## $ Death_ILD_Related : chr [1:161136] "Yes" "Yes" "Yes" "Yes" ...
## $ same_zip : logi [1:161136] TRUE TRUE TRUE TRUE TRUE TRUE ...
## $ zip : chr [1:161136] "95405" "95405" "95405" "95405" ...
## $ zip_new : chr [1:161136] "95405" "95405" "95405" "95405" ...
## $ state : chr [1:161136] "CA" "CA" "CA" "CA" ...
## $ major_city : chr [1:161136] "Santa Rosa" "Santa Rosa" "Santa Rosa" "Santa Rosa" ...
## $ time_censoring : num [1:161136] 0.063 0.063 0.063 0.063 0.063 ...
## $ time_death : num [1:161136] 0.063 0.063 0.063 0.063 0.063 ...
## $ time_tx : num [1:161136] NA NA NA NA NA NA NA NA NA NA ...
## $ time_deathORtx : num [1:161136] 0.063 0.063 0.063 0.063 0.063 ...
## $ time_DeathTxCensor: num [1:161136] 0.063 0.063 0.063 0.063 0.063 ...
## $ PM_5yrPreCensor : num [1:161136] 7.62 7.62 7.62 7.62 7.62 ...
## $ PM_5yrPreDx : num [1:161136] 7.62 7.62 7.62 7.62 7.62 ...
## $ PM5yrCensor_dich : chr [1:161136] "Low" "Low" "Low" "Low" ...
## $ PM5yr_dich : chr [1:161136] "Low" "Low" "Low" "Low" ...
## $ dx_IPF : chr [1:161136] "IPF" "IPF" "IPF" "IPF" ...
## $ ruca : num [1:161136] 1 1 1 1 1 1 1 1 1 1 ...
## $ metro : chr [1:161136] "metropolitan" "metropolitan" "metropolitan" "metropolitan" ...
## $ site : chr [1:161136] "02R" "02R" "02R" "02R" ...
## $ PM_date : Date[1:161136], format: "2000-01-01" "2000-01-01" ...
## $ PM_5yrPreSamp : num [1:161136] 7.62 7.62 7.62 7.62 7.62 ...
## $ PM_1yrPreSamp : num [1:161136] 6.31 6.31 6.31 6.31 6.31 ...
## $ PM_6moPreSamp : num [1:161136] 6.6 6.6 6.6 6.6 6.6 ...
## $ PM_3moPreSamp : num [1:161136] 5.63 5.63 5.63 5.63 5.63 ...
## $ PM_1moPreSamp : num [1:161136] 6.35 6.35 6.35 6.35 6.35 ...
## $ SO4_5yrPreSamp : num [1:161136] 0.673 0.673 0.673 0.673 0.673 ...
## $ SO4_1yrPreSamp : num [1:161136] 0.475 0.475 0.475 0.475 0.475 ...
## $ SO4_6moPreSamp : num [1:161136] 0.417 0.417 0.417 0.417 0.417 ...
## $ SO4_3moPreSamp : num [1:161136] 0.567 0.567 0.567 0.567 0.567 ...
## $ SO4_1moPreSamp : num [1:161136] 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 ...
## $ NO3_5yrPreSamp : num [1:161136] 0.923 0.923 0.923 0.923 0.923 ...
## $ NO3_1yrPreSamp : num [1:161136] 0.8 0.8 0.8 0.8 0.8 ...
## $ NO3_6moPreSamp : num [1:161136] 0.883 0.883 0.883 0.883 0.883 ...
## $ NO3_3moPreSamp : num [1:161136] 0.533 0.533 0.533 0.533 0.533 ...
## $ NO3_1moPreSamp : num [1:161136] 0.6 0.6 0.6 0.6 0.6 ...
## $ NH4_5yrPreSamp : num [1:161136] 0.143 0.143 0.143 0.143 0.143 ...
## $ NH4_1yrPreSamp : num [1:161136] 0.0583 0.0583 0.0583 0.0583 0.0583 ...
## $ NH4_6moPreSamp : num [1:161136] 0.05 0.05 0.05 0.05 0.05 ...
## $ NH4_3moPreSamp : num [1:161136] 0.0333 0.0333 0.0333 0.0333 0.0333 ...
## $ NH4_1moPreSamp : num [1:161136] 0.05 0.05 0.05 0.05 0.05 ...
## $ BC_5yrPreSamp : num [1:161136] 0.738 0.738 0.738 0.738 0.738 ...
## $ BC_1yrPreSamp : num [1:161136] 0.575 0.575 0.575 0.575 0.575 ...
## $ BC_6moPreSamp : num [1:161136] 0.667 0.667 0.667 0.667 0.667 ...
## $ BC_3moPreSamp : num [1:161136] 0.367 0.367 0.367 0.367 0.367 ...
## $ BC_1moPreSamp : num [1:161136] 0.35 0.35 0.35 0.35 0.35 ...
## $ OM_5yrPreSamp : num [1:161136] 3.95 3.95 3.95 3.95 3.95 ...
## $ OM_1yrPreSamp : num [1:161136] 3.08 3.08 3.08 3.08 3.08 ...
## $ OM_6moPreSamp : num [1:161136] 3.52 3.52 3.52 3.52 3.52 ...
## $ OM_3moPreSamp : num [1:161136] 2.1 2.1 2.1 2.1 2.1 ...
## $ OM_1moPreSamp : num [1:161136] 2.1 2.1 2.1 2.1 2.1 ...
## $ SS_5yrPreSamp : num [1:161136] 1.34 1.34 1.34 1.34 1.34 ...
## $ SS_1yrPreSamp : num [1:161136] 1.1 1.1 1.1 1.1 1.1 ...
## $ SS_6moPreSamp : num [1:161136] 1.52 1.52 1.52 1.52 1.52 ...
## $ SS_3moPreSamp : num [1:161136] 2.07 2.07 2.07 2.07 2.07 ...
## $ SS_1moPreSamp : num [1:161136] 2.3 2.3 2.3 2.3 2.3 ...
## $ Soil_date : Date[1:161136], format: "2000-01-01" "2000-02-01" ...
## $ value : num [1:161136] 0.4 0.3 0.3 0.6 0.4 ...10.1 Creating Soil Exposure Variables
Now I’m creating new variables where I am matching up PM averages per year to years of major events for patients (year of diagnosis, year of death/lung transplant/censoring, etc)
10.1.1 5yrs Pre-Sampling
Here I am calculating the average Soil value in the 5yrs prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(Soil_5yrPreSamp = mean(value[ymd(Soil_date)>=(ymd(sample_date) - years(5)) &
ymd(Soil_date)<=ymd(sample_date)]))10.1.2 1yr Pre-Sampling
Here I am calculating the average Soil value in the 1yr prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(Soil_1yrPreSamp = mean(value[ymd(Soil_date)>=(ymd(sample_date) - years(1)) &
ymd(Soil_date)<=ymd(sample_date)]))10.1.3 6mo Pre-Sampling
Here I am calculating the average Soil value in the 6mo prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(Soil_6moPreSamp = mean(value[ymd(Soil_date)>=(ymd(sample_date) - months(6)) &
ymd(Soil_date)<=ymd(sample_date)]))10.1.4 3mo Pre-Sampling
Here I am calculating the average Soil value in the 3mo prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(Soil_3moPreSamp = mean(value[ymd(Soil_date)>=(ymd(sample_date) - months(3)) &
ymd(Soil_date)<=ymd(sample_date)]))10.1.5 1mo Pre-Sampling
Here I am calculating the average Soil value in the 1mo prior to sampling.
dnam <- dnam %>%
group_by(ID) %>%
mutate(Soil_1moPreSamp = mean(value[ymd(Soil_date)>=(ymd(sample_date) - months(2)) &
ymd(Soil_date)<=ymd(sample_date)]))Of note, the 1mo pre-sampling with this method actually provides the average PM2.5 value for the month of sampling and the 1mo prior to sampling because the sampling could have occurred at the beginning or end of a month.
10.1.6 Remove Dataframes and variables not in use
rm(list=c("dnamx", "Soil"))
dnam <- dnam %>% dplyr::select(!c(nrow, dist, lon, lat, value, Soil_date))10.1.7 Getting Rid of Duplicated IDs
Now that we have calculated the patient-specific PM exposures, I can get rid of all rows other than the first row for each patient.
dnam <- dnam %>%
distinct_at(vars(ID), .keep_all=T)This takes us down to 733 observations.
10.2 Reorder columns
dnam <- dnam %>% select(ID, everything(.))11 Export File with All PM2.5 and Constituent Time Frames Matched
write_xlsx(dnam, path="PFF_fILD_PM25andConstituentsMatched_forMethylFlash_2022_04_28.xlsx")12 Releveling Factors
Here I am releveling factors so that they are in an intuitive order for my later analyses.
dnam$sex <- fct_relevel(dnam$sex, c("Male","Female"))
dnam$race <- fct_relevel(dnam$race, c("W","B","A","U"))
dnam$ethnicity <- fct_relevel(dnam$ethnicity, c("N","H","U"))
dnam$dich_Race <- fct_relevel(dnam$dich_Race, c("White","Non-White"))
dnam$smokeHx <- fct_relevel(dnam$smokeHx, c("Never","Ever"))
#For dx and dx_group, I just want IPF to be first and then the rest of the categories are alphabetical
dnam$dx <- fct_relevel(dnam$dx, c("IPF"))
dnam$dx_group <- fct_relevel(dnam$dx_group, c("IPF"))
dnam$dx_IPF <- fct_relevel(dnam$dx_IPF, c("IPF"))
#dnam$PM5yrCensor_dich <- fct_relevel(dnam$PM5yrCensor_dich, c("Low"))
#dnam$PM5yr_dich <- fct_relevel(dnam$PM5yr_dich, c("Low"))
#dnam$SO45yrCensor_dich <- fct_relevel(dnam$SO45yrCensor_dich, c("Low"))
#dnam$SO45yr_dich <- fct_relevel(dnam$SO45yr_dich, c("Low"))
#dnam$NO35yrCensor_dich <- fct_relevel(dnam$NO35yrCensor_dich, c("Low"))
#dnam$NO35yr_dich <- fct_relevel(dnam$NO35yr_dich, c("Low"))
#dnam$NH45yrCensor_dich <- fct_relevel(dnam$NH45yrCensor_dich, c("Low"))
#dnam$NH45yr_dich <- fct_relevel(dnam$NH45yr_dich, c("Low"))
#dnam$BC5yrCensor_dich <- fct_relevel(dnam$BC5yrCensor_dich, c("Low"))
#dnam$BC5yr_dich <- fct_relevel(dnam$BC5yr_dich, c("Low"))
#dnam$OM5yrCensor_dich <- fct_relevel(dnam$OM5yrCensor_dich, c("Low"))
#dnam$OM5yr_dich <- fct_relevel(dnam$OM5yr_dich, c("Low"))
#dnam$SS5yrCensor_dich <- fct_relevel(dnam$SS5yrCensor_dich, c("Low"))
#dnam$SS5yr_dich <- fct_relevel(dnam$SS5yr_dich, c("Low"))
#dnam$Soil5yrCensor_dich <- fct_relevel(dnam$Soil5yrCensor_dich, c("Low"))
#dnam$Soil5yr_dich <- fct_relevel(dnam$Soil5yr_dich, c("Low"))
str(dnam)## grouped_df [746 × 92] (S3: grouped_df/tbl_df/tbl/data.frame)
## $ ID : num [1:746] 513 514 515 516 517 519 520 521 523 524 ...
## $ SSID : chr [1:746] "02R0456" "02R0462" "02R0468" "02R0471" ...
## $ sampID : num [1:746] 77900524 77902774 79049726 77903624 79002826 ...
## $ exptID : num [1:746] 682 324 495 274 267 245 748 15 714 246 ...
## $ run_date : Date[1:746], format: "2022-04-25" "2022-04-12" ...
## $ plate : num [1:746] 17 8 13 7 7 6 19 1 18 6 ...
## $ pct_5mC : num [1:746] 0.1049 0.1397 0.1322 0.0622 0.0492 ...
## $ sex : Factor w/ 2 levels "Male","Female": 1 1 1 2 1 1 1 1 1 1 ...
## $ race : Factor w/ 4 levels "W","B","A","U": 1 1 1 4 1 1 1 1 1 1 ...
## $ dich_Race : Factor w/ 2 levels "White","Non-White": 1 1 1 2 1 1 1 1 1 1 ...
## $ ethnicity : Factor w/ 3 levels "N","H","U": 1 1 1 1 1 1 3 1 1 1 ...
## $ smokeHx : Factor w/ 2 levels "Never","Ever": 2 2 1 2 2 2 1 1 1 2 ...
## $ age_dx : num [1:746] 74 74.2 83.6 74.5 72.3 ...
## $ status : chr [1:746] "1" "0" "0" "1" ...
## $ deadORtx : num [1:746] 1 0 0 1 0 0 0 0 0 0 ...
## $ dx : Factor w/ 1 level "IPF": 1 1 1 1 1 1 1 1 1 1 ...
## $ dx_group : Factor w/ 1 level "IPF": 1 1 1 1 1 1 1 1 1 1 ...
## $ dx_date : Date[1:746], format: "2016-05-20" "2016-07-08" ...
## $ consent_date : Date[1:746], format: "2016-05-19" "2016-07-07" ...
## $ censor_date : Date[1:746], format: "2016-06-11" "2016-07-11" ...
## $ tx_date : Date[1:746], format: NA NA ...
## $ death_date : Date[1:746], format: "2016-06-11" NA ...
## $ deathORtx_date : Date[1:746], format: "2016-06-11" NA ...
## $ DeathTxCensor_date: Date[1:746], format: "2016-06-11" "2016-07-11" ...
## $ sample_date : Date[1:746], format: "2016-05-19" "2016-07-07" ...
## $ fvc_date : Date[1:746], format: "2016-03-17" "2016-04-14" ...
## $ dlco_date : Date[1:746], format: "2016-03-17" "2016-04-14" ...
## $ fvc_pct : num [1:746] 58.5 54.4 NA 81.4 97.2 ...
## $ dlco_pct : num [1:746] 28.8 32.7 NA 46.5 41.1 ...
## $ fvc_timefromdx : num [1:746] -0.175 -0.233 NA 0.832 -0.162 ...
## $ dlco_timefromdx : num [1:746] -0.175 -0.233 NA 0.832 -0.162 ...
## $ Reason_Termination: chr [1:746] "Death" "Lost to Follow-up" NA "Death" ...
## $ Death_ILD_Related : chr [1:746] "Yes" NA NA "Yes" ...
## $ same_zip : logi [1:746] TRUE TRUE TRUE TRUE TRUE TRUE ...
## $ zip : chr [1:746] "95405" "95667" "94578" "94945" ...
## $ zip_new : chr [1:746] "95405" "95667" "94578" "94945" ...
## $ state : chr [1:746] "CA" "CA" "CA" "CA" ...
## $ major_city : chr [1:746] "Santa Rosa" "Placerville" "San Leandro" "Novato" ...
## $ time_censoring : num [1:746] 0.063 0.011 3.392 3.184 1.681 ...
## $ time_death : num [1:746] 0.063 NA NA 3.184 NA ...
## $ time_tx : num [1:746] NA NA NA NA NA NA NA NA NA NA ...
## $ time_deathORtx : num [1:746] 0.063 NA NA 3.184 NA ...
## $ time_DeathTxCensor: num [1:746] 0.063 0.011 3.392 3.184 1.681 ...
## $ PM_5yrPreCensor : num [1:746] 7.62 5.87 10.82 8.52 5.17 ...
## $ PM_5yrPreDx : num [1:746] 7.62 5.87 9.72 8.27 5.48 ...
## $ PM5yrCensor_dich : chr [1:746] "Low" "Low" "High" "High" ...
## $ PM5yr_dich : chr [1:746] "Low" "Low" "High" "High" ...
## $ dx_IPF : Factor w/ 1 level "IPF": 1 1 1 1 1 1 1 1 1 1 ...
## $ ruca : num [1:746] 1 4 1 1 4 1 1 1 1 1 ...
## $ metro : chr [1:746] "metropolitan" "micropolitan" "metropolitan" "metropolitan" ...
## $ site : chr [1:746] "02R" "02R" "02R" "02R" ...
## $ PM_date : Date[1:746], format: "2000-01-01" "2000-01-01" ...
## $ PM_5yrPreSamp : num [1:746] 7.62 5.87 9.28 7.97 5.48 ...
## $ PM_1yrPreSamp : num [1:746] 6.31 4.67 8.3 6.4 4.58 ...
## $ PM_6moPreSamp : num [1:746] 6.6 4.43 9.02 6 5.77 ...
## $ PM_3moPreSamp : num [1:746] 5.63 5.03 9.43 6.27 6.33 ...
## $ PM_1moPreSamp : num [1:746] 6.35 5.25 8.55 6.15 6.7 ...
## $ SO4_5yrPreSamp : num [1:746] 0.673 0.475 0.767 0.69 0.448 ...
## $ SO4_1yrPreSamp : num [1:746] 0.475 0.333 0.708 0.508 0.358 ...
## $ SO4_6moPreSamp : num [1:746] 0.417 0.367 0.5 0.6 0.467 ...
## $ SO4_3moPreSamp : num [1:746] 0.567 0.467 0.433 0.867 0.4 ...
## $ SO4_1moPreSamp : num [1:746] 0.75 0.5 0.5 0.9 0.35 ...
## $ NO3_5yrPreSamp : num [1:746] 0.923 0.545 1.563 1.032 0.307 ...
## $ NO3_1yrPreSamp : num [1:746] 0.8 0.408 1.183 0.825 0.225 ...
## $ NO3_6moPreSamp : num [1:746] 0.883 0.35 1.433 0.717 0.233 ...
## $ NO3_3moPreSamp : num [1:746] 0.533 0.233 1.6 0.6 0.3 ...
## $ NO3_1moPreSamp : num [1:746] 0.6 0.25 1.4 0.6 0.35 ...
## $ NH4_5yrPreSamp : num [1:746] 0.143 0.225 0.305 0.242 0.232 ...
## $ NH4_1yrPreSamp : num [1:746] 0.0583 0.15 0.2417 0.1417 0.2333 ...
## $ NH4_6moPreSamp : num [1:746] 0.05 0.1167 0.3167 0.0833 0.2333 ...
## $ NH4_3moPreSamp : num [1:746] 0.0333 0.0333 0.4 0.0667 0.1667 ...
## $ NH4_1moPreSamp : num [1:746] 0.05 0 0.3 0.05 0.2 ...
## $ BC_5yrPreSamp : num [1:746] 0.738 0.342 0.715 0.557 0.328 ...
## $ BC_1yrPreSamp : num [1:746] 0.575 0.3 0.525 0.517 0.225 ...
## $ BC_6moPreSamp : num [1:746] 0.667 0.2 0.6 0.383 0.283 ...
## $ BC_3moPreSamp : num [1:746] 0.367 0.233 0.667 0.333 0.367 ...
## $ BC_1moPreSamp : num [1:746] 0.35 0.25 0.55 0.35 0.45 ...
## $ OM_5yrPreSamp : num [1:746] 3.95 2.08 3.5 2.62 2.19 ...
## $ OM_1yrPreSamp : num [1:746] 3.08 2.02 3.12 2.46 2.02 ...
## $ OM_6moPreSamp : num [1:746] 3.52 1.47 3.8 2.1 2.9 ...
## $ OM_3moPreSamp : num [1:746] 2.1 1.93 4.23 2.13 4.1 ...
## $ OM_1moPreSamp : num [1:746] 2.1 2.35 3.55 2.25 4.9 ...
## $ SS_5yrPreSamp : num [1:746] 1.345 0.2567 1.8717 1.69 0.0717 ...
## $ SS_1yrPreSamp : num [1:746] 1.1 0.3083 1.7833 1.5583 0.0667 ...
## $ SS_6moPreSamp : num [1:746] 1.5167 0.35 1.1333 2.0667 0.0667 ...
## $ SS_3moPreSamp : num [1:746] 2.0667 0.3 1.2333 2 0.0667 ...
## $ SS_1moPreSamp : num [1:746] 2.3 0.25 0.9 1.85 0 ...
## $ Soil_5yrPreSamp : num [1:746] 0.443 0.39 0.475 0.353 0.512 ...
## $ Soil_1yrPreSamp : num [1:746] 0.433 0.275 0.467 0.317 0.525 ...
## $ Soil_6moPreSamp : num [1:746] 0.383 0.317 0.5 0.333 0.8 ...
## $ Soil_3moPreSamp : num [1:746] 0.4 0.433 0.267 0.433 1.033 ...
## $ Soil_1moPreSamp : num [1:746] 0.5 0.5 0.25 0.4 1.25 ...
## - attr(*, "groups")= tibble [746 × 2] (S3: tbl_df/tbl/data.frame)
## ..$ ID : num [1:746] 513 514 515 516 517 519 520 521 523 524 ...
## ..$ .rows: list<int> [1:746]
## .. ..$ : int 1
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## .. .. [list output truncated]
## .. ..@ ptype: int(0)
## ..- attr(*, ".drop")= logi TRUE13 Creating New Variables
13.1 Time between Registry Enrollment and Sample Date
Need to know if consent_date is a reasonable time point to be basing our DNAm results from
dnam <- dnam %>% mutate(time_sample=(sample_date-consent_date)/365.25)
dnam$time_sample <- as.numeric(dnam$time_sample)14 Proportion %5mC
Will need to use the actual proportion of 5mC for beta regression analyses where values must be between 0 and 1
dnam <- dnam %>% mutate(prop_5mC=pct_5mC/100)15 Exploratory Data Analysis
This is a function that allows me to make tables which summarize the count and percentages of each level of factor variables
n_prop_tbl <- function(x) {
tbl <- table(x)
res <- cbind(tbl, round(prop.table(tbl)*100,2))
colnames(res) <- c('Count', 'Percentage')
res
}15.1 PM Breakdown
print("PM2.5")## [1] "PM2.5"summary(dnam$PM_5yrPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 3.053 7.395 8.280 8.237 9.098 16.142 1summary(dnam$PM_1yrPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 2.642 6.708 7.608 7.526 8.308 15.175 1summary(dnam$PM_6moPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 2.500 6.600 7.617 7.571 8.500 17.967 22summary(dnam$PM_3moPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.967 6.367 7.433 7.506 8.500 20.200 12summary(dnam$PM_1moPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.900 6.300 7.350 7.525 8.700 23.000 6print("/nSO4")## [1] "/nSO4"summary(dnam$SO4_5yrPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.280 1.233 1.542 1.444 1.733 2.445 1summary(dnam$SO4_1yrPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.1750 0.8833 1.1333 1.0955 1.2875 1.9333 5summary(dnam$SO4_6moPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.150 0.850 1.100 1.063 1.283 1.950 40summary(dnam$SO4_3moPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.1667 0.8333 1.0667 1.0615 1.3000 2.1333 125summary(dnam$SO4_1moPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.10 0.80 1.05 1.06 1.30 2.25 143print("/nNO3")## [1] "/nNO3"summary(dnam$NO3_5yrPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.07167 0.49000 0.83667 0.90407 1.31042 4.26333 1summary(dnam$NO3_1yrPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0750 0.4000 0.6583 0.7423 1.0083 3.4667 5summary(dnam$NO3_6moPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0800 0.4000 0.6000 0.7996 0.9833 5.5000 40summary(dnam$NO3_3moPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.3000 0.5333 0.7497 0.9000 7.8000 125summary(dnam$NO3_1moPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.3000 0.5000 0.7328 0.9000 7.4000 143print("/nNH4")## [1] "/nNH4"summary(dnam$NH4_5yrPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.02833 0.32167 0.45500 0.47358 0.60833 1.38000 1summary(dnam$NH4_1yrPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.008333 0.169231 0.250000 0.274247 0.358333 1.066667 5summary(dnam$NH4_6moPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.1667 0.2500 0.2849 0.3500 1.9667 40summary(dnam$NH4_3moPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.1333 0.2333 0.2700 0.3333 2.5000 125summary(dnam$NH4_1moPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.1500 0.2000 0.2623 0.3500 2.2000 143print("/nBC")## [1] "/nBC"summary(dnam$BC_5yrPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.1317 0.4967 0.6133 0.6134 0.7167 1.9650 1summary(dnam$BC_1yrPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.08461 0.47500 0.60000 0.59388 0.70833 1.28571 5summary(dnam$BC_6moPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0500 0.4667 0.6000 0.6240 0.7500 1.8250 40summary(dnam$BC_3moPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.03333 0.43333 0.60000 0.58884 0.73333 1.90000 125summary(dnam$BC_1moPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.4000 0.6000 0.5854 0.7500 2.0000 143print("/nOM")## [1] "/nOM"summary(dnam$OM_5yrPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.7333 2.2650 2.7333 2.8295 3.4267 6.9350 1summary(dnam$OM_1yrPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.8077 2.3500 2.9583 3.0226 3.5667 8.7444 5summary(dnam$OM_6moPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.3167 2.3500 3.0333 3.1523 3.8000 12.6250 40summary(dnam$OM_3moPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.300 2.200 2.967 3.028 3.700 10.133 125summary(dnam$OM_1moPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.200 2.142 2.850 3.042 3.750 10.500 143print("/nSS")## [1] "/nSS"summary(dnam$SS_5yrPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.01333 0.18833 0.24167 0.35948 0.36833 2.52034 1summary(dnam$SS_1yrPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.1833 0.2444 0.3898 0.3923 3.2714 5summary(dnam$SS_6moPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.1333 0.2333 0.3821 0.4167 4.3667 40summary(dnam$SS_3moPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.1333 0.2333 0.3786 0.4333 4.9333 125summary(dnam$SS_1moPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.1000 0.2000 0.3902 0.4500 5.5000 143print("/nSoil")## [1] "/nSoil"summary(dnam$Soil_5yrPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.06833 0.35500 0.49333 0.60401 0.72679 2.42833 1summary(dnam$Soil_1yrPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.05833 0.34167 0.51667 0.58605 0.72500 2.39167 5summary(dnam$Soil_6moPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.03333 0.31667 0.45833 0.57793 0.68333 2.95000 40summary(dnam$Soil_3moPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.03333 0.30000 0.46667 0.59090 0.66667 5.36667 125summary(dnam$Soil_1moPreSamp)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 0.300 0.450 0.593 0.700 7.100 14315.2 Sex Breakdown
n_prop_tbl(dnam$sex)## Count Percentage
## Male 559 74.93
## Female 187 25.0715.3 Race and Ethnicity Breakdown
n_prop_tbl(dnam$race)## Count Percentage
## W 706 94.64
## B 7 0.94
## A 20 2.68
## U 13 1.74n_prop_tbl(dnam$ethnicity)## Count Percentage
## N 702 94.10
## H 29 3.89
## U 15 2.0115.4 Smoking History Breakdown
n_prop_tbl(dnam$smokeHx)## Count Percentage
## Never 267 35.79
## Ever 479 64.2115.5 Diagnostic Group Breakdown
n_prop_tbl(dnam$dx_group)## Count Percentage
## IPF 746 10015.6 State Breakdown
n_prop_tbl(dnam$state)## Count Percentage
## AL 59 7.92
## AR 2 0.27
## AZ 29 3.89
## CA 57 7.65
## CO 7 0.94
## CT 21 2.82
## DC 3 0.40
## DE 3 0.40
## FL 21 2.82
## GA 36 4.83
## IA 2 0.27
## ID 2 0.27
## IL 22 2.95
## IN 6 0.81
## KS 7 0.94
## KY 15 2.01
## LA 17 2.28
## MA 9 1.21
## MD 18 2.42
## ME 1 0.13
## MI 45 6.04
## MN 29 3.89
## MO 14 1.88
## MS 12 1.61
## MT 1 0.13
## NC 23 3.09
## ND 1 0.13
## NE 1 0.13
## NH 6 0.81
## NJ 14 1.88
## NM 3 0.40
## NV 3 0.40
## NY 63 8.46
## OH 28 3.76
## PA 41 5.50
## SC 14 1.88
## SD 1 0.13
## TN 14 1.88
## TX 50 6.71
## UT 6 0.81
## VA 35 4.70
## WI 2 0.27
## WV 2 0.2715.7 Metropolitan Breakdown
n_prop_tbl(dnam$metro)## Count Percentage
## metropolitan 627 84.16
## micropolitan 63 8.46
## rural 55 7.38Overwhelming majority of the patients are considered “metropolitan”.
15.8 Age at Diagnosis Breakdown
shapiro.test(dnam$age_dx)##
## Shapiro-Wilk normality test
##
## data: dnam$age_dx
## W = 0.96994, p-value = 2.961e-11#Shapiro tests tells us that age_dx is not normally distributed, so should report median, IQR
summary(dnam$age_dx)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 18.59 64.15 69.37 68.69 73.95 91.78Not normally distributed.
15.9 Vital Status Breakdown
n_prop_tbl(dnam$status)## Count Percentage
## 0 429 57.51
## 1 213 28.55
## 2 104 13.9415.10 Baseline Lung Function Breakdown
shapiro.test(dnam$fvc_pct)##
## Shapiro-Wilk normality test
##
## data: dnam$fvc_pct
## W = 0.99413, p-value = 0.009972summary(dnam$fvc_pct)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 23.55 55.28 67.10 68.05 79.70 120.43 68sd(dnam$fvc_pct, na.rm=T)## [1] 16.6119shapiro.test(dnam$dlco_pct)##
## Shapiro-Wilk normality test
##
## data: dnam$dlco_pct
## W = 0.92974, p-value < 2.2e-16summary(dnam$dlco_pct)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 8.51 30.12 39.60 40.74 49.39 168.98 108sd(dnam$dlco_pct, na.rm=T)## [1] 15.3361115.11 Time to Censoring Breakdown
summary(dnam$time_DeathTxCensor)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.317 2.359 2.323 3.330 5.060sd(dnam$time_DeathTxCensor, na.rm=T)## [1] 1.29834715.12 Time between registry enrollment and sample collection
summary(dnam$time_sample)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -2.00137 0.00000 0.00000 0.01827 0.00000 1.39904sd(dnam$time_sample, na.rm=T)## [1] 0.1428165This indicates that the majority of samples are taken very close to the time of consent, which is our reference for the _5yrPreDx periods for exposure matching, although there are a few where samples were taken far before or far after the consent_date.
16 Visual Exploratory Data Analysis
16.1 Histograms
Histogram of %5mC
(dnam %>% ggplot(aes(x=pct_5mC))+
geom_histogram(fill="blue", color="black")+
labs(x="% 5mC", y="Number of patients with IPF", title="% 5mC in Patients with IPF")+
theme(plot.title = element_text(hjust = 0.5)))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
We have some very high outliers, whreas the rest is generally <0.3%
Histogram of %5mC
(dnam %>% ggplot(aes(x=pct_5mC))+
geom_histogram(fill="blue", color="black")+
labs(x="% 5mC", y="Number of patients with IPF", title="% 5mC in Patients with IPF")+
theme(plot.title = element_text(hjust = 0.5))+
xlim(0,0.5))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 10 rows containing non-finite values (stat_bin).## Warning: Removed 2 rows containing missing values (geom_bar).
Slightly right-skewed distribution.
16.2 Scatterplots
16.2.1 PM2.5 Scatterplots
Scatterplot of continuous PM2.5 5yrs Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=PM_5yrPreSamp, y=pct_5mC))+
geom_point()+
labs(x="PM2.5 in 5yrs Pre-Sampling", y="% 5mC", title="%5mC by PM2.5 5yrs Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0,20)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 11 rows containing non-finite values (stat_smooth).## Warning: Removed 11 rows containing missing values (geom_point).
Scatterplot of continuous PM2.5 1yr Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=PM_1yrPreSamp, y=pct_5mC))+
geom_point()+
labs(x="PM2.5 in 1yr Pre-Sampling", y="% 5mC", title="%5mC by PM2.5 1yr Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0,20)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 11 rows containing non-finite values (stat_smooth).## Warning: Removed 11 rows containing missing values (geom_point).
Scatterplot of continuous PM2.5 6mo Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=PM_6moPreSamp, y=pct_5mC))+
geom_point()+
labs(x="PM2.5 in 6mo Pre-Sampling", y="% 5mC", title="%5mC by PM2.5 6mo Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0,20)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 31 rows containing non-finite values (stat_smooth).## Warning: Removed 31 rows containing missing values (geom_point).
Scatterplot of continuous PM2.5 3mo Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=PM_3moPreSamp, y=pct_5mC))+
geom_point()+
labs(x="PM2.5 in 3mo Pre-Sampling", y="% 5mC", title="%5mC by PM2.5 3mo Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0,20)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 23 rows containing non-finite values (stat_smooth).## Warning: Removed 23 rows containing missing values (geom_point).
Scatterplot of continuous PM2.5 1mo Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=PM_1moPreSamp, y=pct_5mC))+
geom_point()+
labs(x="PM2.5 in 1mo Pre-Sampling", y="% 5mC", title="%5mC by PM2.5 1mo Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0,20)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 17 rows containing non-finite values (stat_smooth).## Warning: Removed 17 rows containing missing values (geom_point).
16.3 SO4 Scatterplots
Scatterplot of continuous SO4 5yrs Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=SO4_5yrPreSamp, y=pct_5mC))+
geom_point()+
labs(x="SO4 in 5yrs Pre-Sampling", y="% 5mC", title="%5mC by SO4 5yrs Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0,2.5)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 11 rows containing non-finite values (stat_smooth).## Warning: Removed 11 rows containing missing values (geom_point).
Scatterplot of continuous SO4 1yr Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=PM_1yrPreSamp, y=pct_5mC))+
geom_point()+
labs(x="SO4 in 1yr Pre-Sampling", y="% 5mC", title="%5mC by SO4 1yr Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0,20)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 11 rows containing non-finite values (stat_smooth).## Warning: Removed 11 rows containing missing values (geom_point).
Scatterplot of continuous SO4 6mo Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=SO4_6moPreSamp, y=pct_5mC))+
geom_point()+
labs(x="SO4 in 6mo Pre-Sampling", y="% 5mC", title="%5mC by SO4 6mo Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0,2.5)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 49 rows containing non-finite values (stat_smooth).## Warning: Removed 49 rows containing missing values (geom_point).
Scatterplot of continuous SO4 3mo Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=SO4_3moPreSamp, y=pct_5mC))+
geom_point()+
labs(x="SO4 in 3mo Pre-Sampling", y="% 5mC", title="%5mC by SO4 3mo Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0,2.5)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 135 rows containing non-finite values (stat_smooth).## Warning: Removed 135 rows containing missing values (geom_point).
Scatterplot of continuous SO4 1mo Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=SO4_1moPreSamp, y=pct_5mC))+
geom_point()+
labs(x="SO4 in 1mo Pre-Sampling", y="% 5mC", title="%5mC by SO4 1mo Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0,2.5)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 153 rows containing non-finite values (stat_smooth).## Warning: Removed 153 rows containing missing values (geom_point).
16.3.1 NO3 Scatterplots
Scatterplot of continuous NO3 5yrs Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=NO3_5yrPreSamp, y=pct_5mC))+
geom_point()+
labs(x="NO3 in 5yrs Pre-Sampling", y="% 5mC", title="%5mC by NO3 5yrs Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0,5)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 11 rows containing non-finite values (stat_smooth).## Warning: Removed 11 rows containing missing values (geom_point).
Scatterplot of continuous NO3 1yr Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=NO3_1yrPreSamp, y=pct_5mC))+
geom_point()+
labs(x="NO3 in 1yrs Pre-Sampling", y="% 5mC", title="%5mC by NO3 1yrs Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0,5)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 15 rows containing non-finite values (stat_smooth).## Warning: Removed 15 rows containing missing values (geom_point).
Scatterplot of continuous NO3 6mo Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=NO3_6moPreSamp, y=pct_5mC))+
geom_point()+
labs(x="NO3 in 6mo Pre-Sampling", y="% 5mC", title="%5mC by NO3 6mo Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0,5)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 50 rows containing non-finite values (stat_smooth).## Warning: Removed 50 rows containing missing values (geom_point).
Scatterplot of continuous NO3 3mo Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=NO3_3moPreSamp, y=pct_5mC))+
geom_point()+
labs(x="NO3 in 3mo Pre-Sampling", y="% 5mC", title="%5mC by NO3 3mo Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0,5)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 137 rows containing non-finite values (stat_smooth).## Warning: Removed 137 rows containing missing values (geom_point).
Scatterplot of continuous NO3 1mo Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=NO3_1moPreSamp, y=pct_5mC))+
geom_point()+
labs(x="NO3 in 1mo Pre-Sampling", y="% 5mC", title="%5mC by NO3 1mo Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0,5)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 154 rows containing non-finite values (stat_smooth).## Warning: Removed 154 rows containing missing values (geom_point).
16.3.2 NH4 Scatterplots
Scatterplot of continuous NH4 5yrs Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=NH4_5yrPreSamp, y=pct_5mC))+
geom_point()+
labs(x="NH4 in 5yrs Pre-Sampling", y="% 5mC", title="%5mC by NH4 5yrs Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0, 1.5)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 11 rows containing non-finite values (stat_smooth).## Warning: Removed 11 rows containing missing values (geom_point).
Scatterplot of continuous NH4 1yr Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=NH4_1yrPreSamp, y=pct_5mC))+
geom_point()+
labs(x="NH4 in 1yr Pre-Sampling", y="% 5mC", title="%5mC by NH4 1yr Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0, 1.5)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 15 rows containing non-finite values (stat_smooth).## Warning: Removed 15 rows containing missing values (geom_point).
Scatterplot of continuous NH4 6mo Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=NH4_6moPreSamp, y=pct_5mC))+
geom_point()+
labs(x="NH4 in 6mo Pre-Sampling", y="% 5mC", title="%5mC by NH4 6mo Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0,2)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 49 rows containing non-finite values (stat_smooth).## Warning: Removed 49 rows containing missing values (geom_point).
Scatterplot of continuous NH4 3mo Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=NH4_3moPreSamp, y=pct_5mC))+
geom_point()+
labs(x="NH4 in 3mo Pre-Sampling", y="% 5mC", title="%5mC by NH4 3mo Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0,2)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 136 rows containing non-finite values (stat_smooth).## Warning: Removed 136 rows containing missing values (geom_point).
Scatterplot of continuous NH4 1mo Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=NH4_1moPreSamp, y=pct_5mC))+
geom_point()+
labs(x="NH4 in 1mo Pre-Sampling", y="% 5mC", title="%5mC by NH4 1mo Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0,2)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 154 rows containing non-finite values (stat_smooth).## Warning: Removed 154 rows containing missing values (geom_point).
16.3.3 BC Scatterplots
Scatterplot of continuous BC 5yrs Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=BC_5yrPreSamp, y=pct_5mC))+
geom_point()+
labs(x="BC in 5yrs Pre-Sampling", y="% 5mC", title="%5mC by BC 5yrs Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0, 2)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 11 rows containing non-finite values (stat_smooth).## Warning: Removed 11 rows containing missing values (geom_point).
Scatterplot of continuous BC 1yr Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=BC_1yrPreSamp, y=pct_5mC))+
geom_point()+
labs(x="BC in 1yr Pre-Sampling", y="% 5mC", title="%5mC by BC 1yr Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0, 2)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 15 rows containing non-finite values (stat_smooth).## Warning: Removed 15 rows containing missing values (geom_point).
Scatterplot of continuous BC 6mo Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=BC_6moPreSamp, y=pct_5mC))+
geom_point()+
labs(x="BC in 6mo Pre-Sampling", y="% 5mC", title="%5mC by BC 6mo Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0,2)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 49 rows containing non-finite values (stat_smooth).## Warning: Removed 49 rows containing missing values (geom_point).
Scatterplot of continuous BC 3mo Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=BC_3moPreSamp, y=pct_5mC))+
geom_point()+
labs(x="BC in 3mo Pre-Sampling", y="% 5mC", title="%5mC by BC 3mo Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0,2)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 135 rows containing non-finite values (stat_smooth).## Warning: Removed 135 rows containing missing values (geom_point).
Scatterplot of continuous BC 1mo Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=BC_1moPreSamp, y=pct_5mC))+
geom_point()+
labs(x="BC in 1mo Pre-Sampling", y="% 5mC", title="%5mC by BC 1mo Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0,2)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 153 rows containing non-finite values (stat_smooth).## Warning: Removed 153 rows containing missing values (geom_point).
16.3.4 OM Scatterplots
Scatterplot of continuous OM 5yrs Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=OM_5yrPreSamp, y=pct_5mC))+
geom_point()+
labs(x="OM in 5yrs Pre-Sampling", y="% 5mC", title="%5mC by OM 5yrs Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0, 8)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 11 rows containing non-finite values (stat_smooth).## Warning: Removed 11 rows containing missing values (geom_point).
Scatterplot of continuous OM 1yr Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=OM_1yrPreSamp, y=pct_5mC))+
geom_point()+
labs(x="OM in 1yr Pre-Sampling", y="% 5mC", title="%5mC by OM 1yr Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0, 8)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).
Scatterplot of continuous OM 6mo Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=OM_6moPreSamp, y=pct_5mC))+
geom_point()+
labs(x="OM in 6mo Pre-Sampling", y="% 5mC", title="%5mC by OM 6mo Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0,10)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 50 rows containing non-finite values (stat_smooth).## Warning: Removed 50 rows containing missing values (geom_point).
Scatterplot of continuous OM 3mo Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=OM_3moPreSamp, y=pct_5mC))+
geom_point()+
labs(x="OM in 3mo Pre-Sampling", y="% 5mC", title="%5mC by OM 3mo Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0,10)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 136 rows containing non-finite values (stat_smooth).## Warning: Removed 136 rows containing missing values (geom_point).
Scatterplot of continuous OM 1mo Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=OM_1moPreSamp, y=pct_5mC))+
geom_point()+
labs(x="OM in 1mo Pre-Sampling", y="% 5mC", title="%5mC by OM 1mo Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0,10)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 154 rows containing non-finite values (stat_smooth).## Warning: Removed 154 rows containing missing values (geom_point).
16.4 SS Scatterplots
Scatterplot of continuous SS 5yrs Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=SS_5yrPreSamp, y=pct_5mC))+
geom_point()+
labs(x="SS in 5yrs Pre-Sampling", y="% 5mC", title="%5mC by SS 5yrs Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0, 2.5)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 12 rows containing non-finite values (stat_smooth).## Warning: Removed 12 rows containing missing values (geom_point).
Scatterplot of continuous SS 1yr Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=SS_1yrPreSamp, y=pct_5mC))+
geom_point()+
labs(x="SS in 1yr Pre-Sampling", y="% 5mC", title="%5mC by SS 1yr Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0, 2.5)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 25 rows containing non-finite values (stat_smooth).## Warning: Removed 25 rows containing missing values (geom_point).
Scatterplot of continuous SS 6mo Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=SS_6moPreSamp, y=pct_5mC))+
geom_point()+
labs(x="SS in 6mo Pre-Sampling", y="% 5mC", title="%5mC by SS 6mo Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0,5)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 49 rows containing non-finite values (stat_smooth).## Warning: Removed 49 rows containing missing values (geom_point).
Scatterplot of continuous SS 3mo Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=SS_3moPreSamp, y=pct_5mC))+
geom_point()+
labs(x="SS in 3mo Pre-Sampling", y="% 5mC", title="%5mC by SS 3mo Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0,5)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 135 rows containing non-finite values (stat_smooth).## Warning: Removed 135 rows containing missing values (geom_point).
Scatterplot of continuous SS 1mo Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=SS_1moPreSamp, y=pct_5mC))+
geom_point()+
labs(x="SS in 1mo Pre-Sampling", y="% 5mC", title="%5mC by SS 1mo Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0,5)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 154 rows containing non-finite values (stat_smooth).## Warning: Removed 154 rows containing missing values (geom_point).
16.4.1 Soil Scatterplots
Scatterplot of continuous Soil 5yrs Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=Soil_5yrPreSamp, y=pct_5mC))+
geom_point()+
labs(x="Soil in 5yrs Pre-Sampling", y="% 5mC", title="%5mC by Soil 5yrs Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0, 3)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 11 rows containing non-finite values (stat_smooth).## Warning: Removed 11 rows containing missing values (geom_point).
Scatterplot of continuous Soil 1yr Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=Soil_1yrPreSamp, y=pct_5mC))+
geom_point()+
labs(x="Soil in 1yr Pre-Sampling", y="% 5mC", title="%5mC by Soil 1yr Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0, 3)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 15 rows containing non-finite values (stat_smooth).## Warning: Removed 15 rows containing missing values (geom_point).
Scatterplot of continuous Soil 6mo Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=Soil_6moPreSamp, y=pct_5mC))+
geom_point()+
labs(x="Soil in 6mo Pre-Sampling", y="% 5mC", title="%5mC by Soil 6mo Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0,3)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 49 rows containing non-finite values (stat_smooth).## Warning: Removed 49 rows containing missing values (geom_point).
Scatterplot of continuous Soil 3mo Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=Soil_3moPreSamp, y=pct_5mC))+
geom_point()+
labs(x="Soil in 3mo Pre-Sampling", y="% 5mC", title="%5mC by Soil 3mo Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0,3)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 136 rows containing non-finite values (stat_smooth).## Warning: Removed 136 rows containing missing values (geom_point).
Scatterplot of continuous Soil 1mo Pre-Sampling vs %5mC
(dnam %>% ggplot(aes(x=Soil_1moPreSamp, y=pct_5mC))+
geom_point()+
labs(x="Soil in 1mo Pre-Sampling", y="% 5mC", title="%5mC by Soil 1mo Pre-Sampling")+
geom_smooth(method="lm", se = FALSE) +
theme(plot.title = element_text(hjust = 0.5))+
xlim(0,3)+
ylim(0, 0.5))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 155 rows containing non-finite values (stat_smooth).## Warning: Removed 155 rows containing missing values (geom_point).
16.4.2 Violin Plots
Violin plot wrapping boxplot to visualize NO3 5yrs Pre-Sampling low vs high vs %5mC
#dnamb <- dnam %>% filter(!is.na(NO35yrCensor_dich))
#(dnamb %>% ggplot(aes(x=NO35yrCensor_dich, y=pct_5mC, fill=NO35yrCensor_dich))+
# geom_boxplot(width=0.2, color="black", alpha=1.0)+
# geom_violin(width=1.0, alpha=0.5)+
# labs(x="NO3 5yrs Pre-Sampling Low vs High", y="% 5mC", title="% 5mC by NO3 5yrs Pre-Sampling Low vs High")+
# theme_light()+
# theme(legend.position = "none", plot.title = element_text(hjust = 0.5))+
# scale_fill_brewer(type="seq", palette="YlOrRd")+
# ylim(0,0.25))Violin plot wrapping boxplot to visualize NO3 5yrs pre-dx low vs high vs %5mC
#dnamb <- dnam %>% filter(!is.na(NO35yr_dich))
#(dnamb %>% ggplot(aes(x=NO35yr_dich, y=pct_5mC, fill=NO35yr_dich))+
# geom_boxplot(width=0.2, color="black", alpha=1.0)+
# geom_violin(width=1.0, alpha=0.5)+
# labs(x="NO3 1yr Pre-Sampling Low vs High", y="% 5mC", title="% 5mC by NO3 1yr Pre-Sampling Low vs High")+
# theme_light()+
# theme(legend.position = "none", plot.title = element_text(hjust = 0.5))+
# scale_fill_brewer(type="seq", palette="YlOrRd")+
# ylim(0,0.25))Violin plot wrapping boxplot to visualize NH4 5yrs Pre-Sampling low vs high vs %5mC
#dnamb <- dnam %>% filter(!is.na(NH45yrCensor_dich))
#(dnamb %>% ggplot(aes(x=NH45yrCensor_dich, y=pct_5mC, fill=NH45yrCensor_dich))+
# geom_boxplot(width=0.2, color="black", alpha=1.0)+
# geom_violin(width=1.0, alpha=0.5)+
# labs(x="NH4 5yrs Pre-Sampling Low vs High", y="% 5mC", title="% 5mC by NH4 5yrs Pre-Sampling Low vs High")+
# theme_light()+
# theme(legend.position = "none", plot.title = element_text(hjust = 0.5))+
# scale_fill_brewer(type="seq", palette="YlOrRd")+
# ylim(0,0.25))Violin plot wrapping boxplot to visualize NH4 5yrs pre-dx low vs high vs %5mC
#dnamb <- dnam %>% filter(!is.na(NH45yr_dich))
#(dnamb %>% ggplot(aes(x=NH45yr_dich, y=pct_5mC, fill=NH45yr_dich))+
# geom_boxplot(width=0.2, color="black", alpha=1.0)+
# geom_violin(width=1.0, alpha=0.5)+
# labs(x="NH4 1yr Pre-Sampling Low vs High", y="% 5mC", title="% 5mC by NH4 1yr Pre-Sampling Low vs High")+
# theme_light()+
# theme(legend.position = "none", plot.title = element_text(hjust = 0.5))+
# scale_fill_brewer(type="seq", palette="YlOrRd")+
# ylim(0,0.25))17 Linear Regression Analyses
17.1 PM2.5 Models
###Continuous PM2.5 5yr Pre-Censor
dnam_model1 <- betareg(prop_5mC ~ PM_5yrPreSamp, dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ PM_5yrPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0934 -0.3967 -0.0721 0.2850 5.3443
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.81077 0.11701 -58.209 <2e-16 ***
## PM_5yrPreSamp 0.01314 0.01389 0.946 0.344
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2142.6 115.4 18.57 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4401 on 3 Df
## Pseudo R-squared: 0.002777
## Number of iterations: 215 (BFGS) + 5 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -7.04010171 -6.58144546
## PM_5yrPreSamp -0.01407672 0.04036428
## (phi) 1916.52576149 2368.69237872Impact of sex on model
dnam_model1 <- betareg(prop_5mC ~ PM_5yrPreSamp + sex, dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ PM_5yrPreSamp + sex, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0824 -0.4035 -0.0728 0.2854 5.3383
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.81827 0.11803 -57.768 <2e-16 ***
## PM_5yrPreSamp 0.01338 0.01389 0.963 0.336
## sexFemale 0.02206 0.04717 0.468 0.640
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2143.3 115.4 18.57 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4401 on 4 Df
## Pseudo R-squared: 0.003426
## Number of iterations: 720 (BFGS) + 3 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -7.04960352 -6.58693849
## PM_5yrPreSamp -0.01385524 0.04060739
## sexFemale -0.07038627 0.11450638
## (phi) 1917.12407737 2369.42710691Impact of age_dx on model
dnam_model1 <- betareg(prop_5mC ~ PM_5yrPreSamp + age_dx, dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ PM_5yrPreSamp + age_dx, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1140 -0.3988 -0.0680 0.2808 5.3697
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.715667 0.213437 -31.464 <2e-16 ***
## PM_5yrPreSamp 0.013071 0.013889 0.941 0.347
## age_dx -0.001377 0.002589 -0.532 0.595
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2143.5 115.4 18.57 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4401 on 4 Df
## Pseudo R-squared: 0.003644
## Number of iterations: 515 (BFGS) + 4 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -7.133996e+00 -6.297338e+00
## PM_5yrPreSamp -1.415025e-02 4.029250e-02
## age_dx -6.450539e-03 3.697292e-03
## (phi) 1.917298e+03 2.369640e+03Impact of smokeHx on model
dnam_model1 <- betareg(prop_5mC ~ PM_5yrPreSamp + smokeHx, dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ PM_5yrPreSamp + smokeHx, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1297 -0.4123 -0.0766 0.2778 5.3355
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.79638 0.12019 -56.547 <2e-16 ***
## PM_5yrPreSamp 0.01321 0.01389 0.951 0.342
## smokeHxEver -0.02331 0.04272 -0.546 0.585
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2143.5 115.4 18.57 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4401 on 4 Df
## Pseudo R-squared: 0.003651
## Number of iterations: 474 (BFGS) + 3 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -7.03194766 -6.56081064
## PM_5yrPreSamp -0.01401575 0.04043148
## smokeHxEver -0.10704887 0.06042998
## (phi) 1917.34421122 2369.69747431Impact of race on model
dnam_model1 <- betareg(prop_5mC ~ PM_5yrPreSamp + dich_Race, dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ PM_5yrPreSamp + dich_Race, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0941 -0.3969 -0.0721 0.2849 5.3444
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.8108778 0.1178962 -57.770 <2e-16 ***
## PM_5yrPreSamp 0.0131611 0.0140862 0.934 0.350
## dich_RaceNon-White -0.0007183 0.0919231 -0.008 0.994
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2142.6 115.4 18.57 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4401 on 4 Df
## Pseudo R-squared: 0.002778
## Number of iterations: 150 (BFGS) + 3 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -7.0419501 -6.57980537
## PM_5yrPreSamp -0.0144473 0.04076952
## dich_RaceNon-White -0.1808843 0.17944763
## (phi) 1916.5258892 2368.69253558Impact of urbanicity on model
dnam_model1 <- betareg(prop_5mC ~ PM_5yrPreSamp + metro, dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ PM_5yrPreSamp + metro, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1020 -0.3935 -0.0688 0.2842 5.3570
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.78465 0.12441 -54.533 <2e-16 ***
## PM_5yrPreSamp 0.01066 0.01447 0.737 0.461
## metromicropolitan -0.03226 0.07607 -0.424 0.672
## metrorural -0.03060 0.08223 -0.372 0.710
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2142.5 115.4 18.56 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4395 on 5 Df
## Pseudo R-squared: 0.003558
## Number of iterations: 271 (BFGS) + 3 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -7.02850159 -6.54080491
## PM_5yrPreSamp -0.01769165 0.03901512
## metromicropolitan -0.18135830 0.11684721
## metrorural -0.19177756 0.13056969
## (phi) 1916.24680205 2368.67427891Partial model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ PM_5yrPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ PM_5yrPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1387 -0.4058 -0.0784 0.2810 5.3563
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.713922 0.217764 -30.831 <2e-16 ***
## PM_5yrPreSamp 0.013304 0.013896 0.957 0.338
## sexFemale 0.017245 0.047671 0.362 0.718
## age_dx -0.001293 0.002598 -0.498 0.619
## smokeHxEver -0.021535 0.043021 -0.501 0.617
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2144.8 115.5 18.57 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4401 on 6 Df
## Pseudo R-squared: 0.004893
## Number of iterations: 225 (BFGS) + 3 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -7.140731e+00 -6.287113e+00
## PM_5yrPreSamp -1.393286e-02 4.054033e-02
## sexFemale -7.618916e-02 1.106785e-01
## age_dx -6.384218e-03 3.798731e-03
## smokeHxEver -1.058546e-01 6.278466e-02
## (phi) 1.918478e+03 2.371090e+03No significant association between PM_5yrPreSamp and prop_5mC in this model.
Complete model with age_dx, sex, smokeHx, race, urbanicity
dnam_model3 <- betareg(prop_5mC ~ PM_5yrPreSamp + sex + age_dx + smokeHx + dich_Race + metro, data=dnam)
summary(dnam_model3)##
## Call:
## betareg(formula = prop_5mC ~ PM_5yrPreSamp + sex + age_dx + smokeHx +
## dich_Race + metro, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1533 -0.4031 -0.0802 0.2751 5.3672
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.692229 0.222963 -30.015 <2e-16 ***
## PM_5yrPreSamp 0.010820 0.014640 0.739 0.460
## sexFemale 0.016874 0.047773 0.353 0.724
## age_dx -0.001199 0.002604 -0.460 0.645
## smokeHxEver -0.023908 0.043118 -0.554 0.579
## dich_RaceNon-White -0.004606 0.092172 -0.050 0.960
## metromicropolitan -0.030009 0.076190 -0.394 0.694
## metrorural -0.035227 0.082435 -0.427 0.669
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2144.7 115.5 18.56 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4395 on 9 Df
## Pseudo R-squared: 0.005725
## Number of iterations: 156 (BFGS) + 3 (Fisher scoring)confint(dnam_model3)## 2.5 % 97.5 %
## (Intercept) -7.129228e+00 -6.255230e+00
## PM_5yrPreSamp -1.787368e-02 3.951281e-02
## sexFemale -7.675881e-02 1.105067e-01
## age_dx -6.301724e-03 3.904336e-03
## smokeHxEver -1.084168e-01 6.060122e-02
## dich_RaceNon-White -1.852603e-01 1.760482e-01
## metromicropolitan -1.793379e-01 1.193196e-01
## metrorural -1.967969e-01 1.263424e-01
## (phi) 1.918246e+03 2.371130e+03No significant association between PM_5yrPreSamp and prop_5mC in this model.
17.1.1 Continuous PM2.5 1yr Pre-Sampling
dnam_model1 <- betareg(prop_5mC ~ PM_1yrPreSamp, dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ PM_1yrPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1109 -0.4071 -0.0763 0.2810 5.3473
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.79952 0.10347 -65.712 <2e-16 ***
## PM_1yrPreSamp 0.01289 0.01336 0.965 0.335
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2142.7 115.4 18.57 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4401 on 3 Df
## Pseudo R-squared: 0.002929
## Number of iterations: 5000 (BFGS) + 3 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -7.0023218 -6.5967111
## PM_1yrPreSamp -0.0133012 0.0390802
## (phi) 1916.6356029 2368.8271574Impact of sex on model
dnam_model1 <- betareg(prop_5mC ~ PM_1yrPreSamp + sex, dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ PM_1yrPreSamp + sex, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0982 -0.3999 -0.0738 0.2811 5.3410
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.80760 0.10479 -64.961 <2e-16 ***
## PM_1yrPreSamp 0.01320 0.01338 0.987 0.324
## sexFemale 0.02266 0.04719 0.480 0.631
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2143.4 115.4 18.57 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4401 on 4 Df
## Pseudo R-squared: 0.003613
## Number of iterations: 285 (BFGS) + 4 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -7.01299567 -6.60220831
## PM_1yrPreSamp -0.01301943 0.03942261
## sexFemale -0.06983010 0.11514772
## (phi) 1917.26631533 2369.60167981Impact of age_dx on model
dnam_model1 <- betareg(prop_5mC ~ PM_1yrPreSamp + age_dx, dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ PM_1yrPreSamp + age_dx, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1333 -0.4007 -0.0732 0.2817 5.3724
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.705407 0.207456 -32.322 <2e-16 ***
## PM_1yrPreSamp 0.012769 0.013366 0.955 0.339
## age_dx -0.001358 0.002589 -0.524 0.600
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2143.6 115.4 18.57 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4401 on 4 Df
## Pseudo R-squared: 0.003753
## Number of iterations: 357 (BFGS) + 3 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -7.112013e+00 -6.298801e+00
## PM_1yrPreSamp -1.342809e-02 3.896698e-02
## age_dx -6.432495e-03 3.717159e-03
## (phi) 1.917389e+03 2.369752e+03Impact of smokeHx on model
dnam_model1 <- betareg(prop_5mC ~ PM_1yrPreSamp + smokeHx, dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ PM_1yrPreSamp + smokeHx, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1493 -0.4087 -0.0810 0.2831 5.3387
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.78488 0.10702 -63.397 <2e-16 ***
## PM_1yrPreSamp 0.01291 0.01336 0.966 0.334
## smokeHxEver -0.02308 0.04273 -0.540 0.589
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2143.6 115.4 18.57 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4401 on 4 Df
## Pseudo R-squared: 0.003806
## Number of iterations: 274 (BFGS) + 3 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.99464424 -6.57512435
## PM_1yrPreSamp -0.01328436 0.03910143
## smokeHxEver -0.10681696 0.06066362
## (phi) 1917.43483409 2369.80859211Impact of race on model
dnam_model1 <- betareg(prop_5mC ~ PM_1yrPreSamp + dich_Race, dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ PM_1yrPreSamp + dich_Race, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1111 -0.4072 -0.0763 0.2810 5.3474
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.7995895 0.1041294 -65.299 <2e-16 ***
## PM_1yrPreSamp 0.0129037 0.0135434 0.953 0.341
## dich_RaceNon-White -0.0006357 0.0918550 -0.007 0.994
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2142.7 115.4 18.57 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4401 on 4 Df
## Pseudo R-squared: 0.00293
## Number of iterations: 177 (BFGS) + 3 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -7.00367933 -6.59549963
## PM_1yrPreSamp -0.01364075 0.03944825
## dich_RaceNon-White -0.18066813 0.17939669
## (phi) 1916.63568858 2368.82726247Impact of urbanicity on model
dnam_model1 <- betareg(prop_5mC ~ PM_1yrPreSamp + metro, dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ PM_1yrPreSamp + metro, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1177 -0.3922 -0.0746 0.2870 5.3590
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.77512 0.11059 -61.265 <2e-16 ***
## PM_1yrPreSamp 0.01039 0.01395 0.745 0.456
## metromicropolitan -0.03171 0.07618 -0.416 0.677
## metrorural -0.03040 0.08224 -0.370 0.712
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2142.5 115.4 18.56 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4395 on 5 Df
## Pseudo R-squared: 0.003622
## Number of iterations: 656 (BFGS) + 3 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.99187109 -6.55837723
## PM_1yrPreSamp -0.01694166 0.03772986
## metromicropolitan -0.18101946 0.11759671
## metrorural -0.19159631 0.13079369
## (phi) 1916.28876554 2368.72575737Partial model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ PM_1yrPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ PM_1yrPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1577 -0.3981 -0.0836 0.2781 5.3590
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.704267 0.212068 -31.614 <2e-16 ***
## PM_1yrPreSamp 0.013032 0.013384 0.974 0.330
## sexFemale 0.017901 0.047697 0.375 0.707
## age_dx -0.001271 0.002598 -0.489 0.625
## smokeHxEver -0.021241 0.043021 -0.494 0.621
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2144.9 115.5 18.57 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4402 on 6 Df
## Pseudo R-squared: 0.005034
## Number of iterations: 333 (BFGS) + 4 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -7.119913e+00 -6.288621e+00
## PM_1yrPreSamp -1.320018e-02 3.926347e-02
## sexFemale -7.558237e-02 1.113852e-01
## age_dx -6.363744e-03 3.821857e-03
## smokeHxEver -1.055617e-01 6.307937e-02
## (phi) 1.918578e+03 2.371213e+03No significant association between PM_1yrPreSamp and prop_5mC in this model.
Complete model with age_dx, sex, smokeHx, race, urbanicity
dnam_model3 <- betareg(prop_5mC ~ PM_1yrPreSamp + sex + age_dx + smokeHx + dich_Race + metro, data=dnam)
summary(dnam_model3)##
## Call:
## betareg(formula = prop_5mC ~ PM_1yrPreSamp + sex + age_dx + smokeHx +
## dich_Race + metro, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1685 -0.3993 -0.0758 0.2756 5.3692
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.683713 0.216686 -30.845 <2e-16 ***
## PM_1yrPreSamp 0.010522 0.014118 0.745 0.456
## sexFemale 0.017410 0.047792 0.364 0.716
## age_dx -0.001184 0.002605 -0.454 0.650
## smokeHxEver -0.023639 0.043119 -0.548 0.584
## dich_RaceNon-White -0.004422 0.092118 -0.048 0.962
## metromicropolitan -0.029512 0.076288 -0.387 0.699
## metrorural -0.035065 0.082441 -0.425 0.671
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2144.7 115.5 18.56 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4395 on 9 Df
## Pseudo R-squared: 0.005782
## Number of iterations: 133 (BFGS) + 2 (Fisher scoring)confint(dnam_model3)## 2.5 % 97.5 %
## (Intercept) -7.108411e+00 -6.259016e+00
## PM_1yrPreSamp -1.714769e-02 3.819216e-02
## sexFemale -7.626092e-02 1.110812e-01
## age_dx -6.288497e-03 3.921366e-03
## smokeHxEver -1.081502e-01 6.087158e-02
## dich_RaceNon-White -1.849689e-01 1.761255e-01
## metromicropolitan -1.790345e-01 1.200097e-01
## metrorural -1.966460e-01 1.265169e-01
## (phi) 1.918279e+03 2.371170e+03No significant association between PM_1yrPreSamp and prop_5mC in this model.
17.1.2 Continuous PM2.5 5yr Pre-Sample
dnam_model1 <- betareg(prop_5mC ~ PM_5yrPreSamp, dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ PM_5yrPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0934 -0.3967 -0.0721 0.2850 5.3443
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.81077 0.11701 -58.209 <2e-16 ***
## PM_5yrPreSamp 0.01314 0.01389 0.946 0.344
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2142.6 115.4 18.57 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4401 on 3 Df
## Pseudo R-squared: 0.002777
## Number of iterations: 215 (BFGS) + 5 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -7.04010171 -6.58144546
## PM_5yrPreSamp -0.01407672 0.04036428
## (phi) 1916.52576149 2368.69237872No significant association between PM_5yrPreSamp and prop_5mC in this model.
Partial model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ PM_5yrPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ PM_5yrPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1387 -0.4058 -0.0784 0.2810 5.3563
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.713922 0.217764 -30.831 <2e-16 ***
## PM_5yrPreSamp 0.013304 0.013896 0.957 0.338
## sexFemale 0.017245 0.047671 0.362 0.718
## age_dx -0.001293 0.002598 -0.498 0.619
## smokeHxEver -0.021535 0.043021 -0.501 0.617
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2144.8 115.5 18.57 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4401 on 6 Df
## Pseudo R-squared: 0.004893
## Number of iterations: 225 (BFGS) + 3 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -7.140731e+00 -6.287113e+00
## PM_5yrPreSamp -1.393286e-02 4.054033e-02
## sexFemale -7.618916e-02 1.106785e-01
## age_dx -6.384218e-03 3.798731e-03
## smokeHxEver -1.058546e-01 6.278466e-02
## (phi) 1.918478e+03 2.371090e+03No significant association between PM_5yrPreSamp and prop_5mC in this model.
Complete model with age_dx, sex, smokeHx, race, urbanicity
dnam_model3 <- betareg(prop_5mC ~ PM_5yrPreSamp + sex + age_dx + smokeHx + dich_Race + metro, data=dnam)
summary(dnam_model3)##
## Call:
## betareg(formula = prop_5mC ~ PM_5yrPreSamp + sex + age_dx + smokeHx +
## dich_Race + metro, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1533 -0.4031 -0.0802 0.2751 5.3672
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.692229 0.222963 -30.015 <2e-16 ***
## PM_5yrPreSamp 0.010820 0.014640 0.739 0.460
## sexFemale 0.016874 0.047773 0.353 0.724
## age_dx -0.001199 0.002604 -0.460 0.645
## smokeHxEver -0.023908 0.043118 -0.554 0.579
## dich_RaceNon-White -0.004606 0.092172 -0.050 0.960
## metromicropolitan -0.030009 0.076190 -0.394 0.694
## metrorural -0.035227 0.082435 -0.427 0.669
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2144.7 115.5 18.56 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4395 on 9 Df
## Pseudo R-squared: 0.005725
## Number of iterations: 156 (BFGS) + 3 (Fisher scoring)confint(dnam_model3)## 2.5 % 97.5 %
## (Intercept) -7.129228e+00 -6.255230e+00
## PM_5yrPreSamp -1.787368e-02 3.951281e-02
## sexFemale -7.675881e-02 1.105067e-01
## age_dx -6.301724e-03 3.904336e-03
## smokeHxEver -1.084168e-01 6.060122e-02
## dich_RaceNon-White -1.852603e-01 1.760482e-01
## metromicropolitan -1.793379e-01 1.193196e-01
## metrorural -1.967969e-01 1.263424e-01
## (phi) 1.918246e+03 2.371130e+03No significant association between PM_5yrPreSamp and prop_5mC in this model.
17.1.3 Continuous PM2.5 6mo Pre-Sample
dnam_model1 <- betareg(prop_5mC ~ PM_6moPreSamp, dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ PM_6moPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0820 -0.3952 -0.0748 0.2728 5.3638
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.759250 0.090423 -74.751 <2e-16 ***
## PM_6moPreSamp 0.006491 0.011520 0.563 0.573
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2157.1 117.8 18.31 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4282 on 3 Df
## Pseudo R-squared: 0.001044
## Number of iterations: 2160 (BFGS) + 4 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.93647652 -6.5820237
## PM_6moPreSamp -0.01608737 0.0290689
## (phi) 1926.21409828 2388.0122850No significant association between PM_6moPreSamp and prop_5mC in this model.
Partial model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ PM_6moPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ PM_6moPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1575 -0.3876 -0.0801 0.2805 5.3788
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.646045 0.205864 -32.284 <2e-16 ***
## PM_6moPreSamp 0.006454 0.011550 0.559 0.576
## sexFemale 0.006335 0.048713 0.130 0.897
## age_dx -0.001412 0.002618 -0.539 0.590
## smokeHxEver -0.027536 0.043564 -0.632 0.527
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2159.5 117.9 18.31 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4283 on 6 Df
## Pseudo R-squared: 0.003397
## Number of iterations: 86 (BFGS) + 3 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -7.049531e+00 -6.242558e+00
## PM_6moPreSamp -1.618385e-02 2.909232e-02
## sexFemale -8.914068e-02 1.018113e-01
## age_dx -6.543327e-03 3.719134e-03
## smokeHxEver -1.129206e-01 5.784837e-02
## (phi) 1.928313e+03 2.390597e+03No significant association between PM_6moPreSamp and prop_5mC in this model.
Complete model with age_dx, sex, smokeHx, race, urbanicity
dnam_model3 <- betareg(prop_5mC ~ PM_6moPreSamp + sex + age_dx + smokeHx + dich_Race + metro, data=dnam)
summary(dnam_model3)##
## Call:
## betareg(formula = prop_5mC ~ PM_6moPreSamp + sex + age_dx + smokeHx +
## dich_Race + metro, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1572 -0.3906 -0.0763 0.2773 5.3877
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.629700 0.208498 -31.797 <2e-16 ***
## PM_6moPreSamp 0.004332 0.012006 0.361 0.718
## sexFemale 0.005841 0.048819 0.120 0.905
## age_dx -0.001314 0.002624 -0.501 0.616
## smokeHxEver -0.029603 0.043674 -0.678 0.498
## dich_RaceNon-White 0.008271 0.091605 0.090 0.928
## metromicropolitan -0.032440 0.077340 -0.419 0.675
## metrorural -0.034537 0.082502 -0.419 0.675
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2160 118 18.3 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4276 on 9 Df
## Pseudo R-squared: 0.004416
## Number of iterations: 158 (BFGS) + 4 (Fisher scoring)confint(dnam_model3)## 2.5 % 97.5 %
## (Intercept) -7.03834815 -6.221051e+00
## PM_6moPreSamp -0.01919819 2.786296e-02
## sexFemale -0.08984261 1.015238e-01
## age_dx -0.00645720 3.828396e-03
## smokeHxEver -0.11520271 5.599681e-02
## dich_RaceNon-White -0.17127119 1.878129e-01
## metromicropolitan -0.18402242 1.191431e-01
## metrorural -0.19623758 1.271636e-01
## (phi) 1928.20685526 2.390809e+03No significant association between PM_6moPreSamp and prop_5mC in this model.
17.1.4 Continuous PM2.5 3mo Pre-Sample
dnam_model1 <- betareg(prop_5mC ~ PM_3moPreSamp, dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ PM_3moPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0748 -0.3977 -0.0704 0.2855 5.3533
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.745942 0.081576 -82.695 <2e-16 ***
## PM_3moPreSamp 0.005387 0.010406 0.518 0.605
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2146.3 116.4 18.44 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4338 on 3 Df
## Pseudo R-squared: 0.0008507
## Number of iterations: 879 (BFGS) + 2 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.90582786 -6.58605623
## PM_3moPreSamp -0.01500861 0.02578313
## (phi) 1918.09570173 2374.43953470No significant association between PM_3moPreSamp and prop_5mC in this model.
Partial model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ PM_3moPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ PM_3moPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1420 -0.3905 -0.0779 0.2829 5.3678
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.635689 0.204009 -32.526 <2e-16 ***
## PM_3moPreSamp 0.005204 0.010431 0.499 0.618
## sexFemale 0.003833 0.048501 0.079 0.937
## age_dx -0.001333 0.002616 -0.510 0.610
## smokeHxEver -0.028777 0.043304 -0.665 0.506
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2148.5 116.5 18.44 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4338 on 6 Df
## Pseudo R-squared: 0.003115
## Number of iterations: 86 (BFGS) + 3 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -7.035540e+00 -6.235837e+00
## PM_3moPreSamp -1.523952e-02 2.564756e-02
## sexFemale -9.122724e-02 9.889350e-02
## age_dx -6.461128e-03 3.794611e-03
## smokeHxEver -1.136512e-01 5.609780e-02
## (phi) 1.920129e+03 2.376940e+03No significant association between PM_3moPreSamp and prop_5mC in this model.
Complete model with age_dx, sex, smokeHx, race, urbanicity
dnam_model3 <- betareg(prop_5mC ~ PM_3moPreSamp + sex + age_dx + smokeHx + dich_Race + metro, data=dnam)
summary(dnam_model3)##
## Call:
## betareg(formula = prop_5mC ~ PM_3moPreSamp + sex + age_dx + smokeHx +
## dich_Race + metro, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1591 -0.3939 -0.0642 0.2837 5.3792
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.620824 0.205968 -32.145 <2e-16 ***
## PM_3moPreSamp 0.003726 0.010644 0.350 0.726
## sexFemale 0.003528 0.048581 0.073 0.942
## age_dx -0.001276 0.002622 -0.487 0.627
## smokeHxEver -0.031045 0.043403 -0.715 0.474
## dich_RaceNon-White 0.005997 0.091259 0.066 0.948
## metromicropolitan -0.032043 0.076177 -0.421 0.674
## metrorural -0.042457 0.081949 -0.518 0.604
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2149.0 116.6 18.43 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4332 on 9 Df
## Pseudo R-squared: 0.004439
## Number of iterations: 144 (BFGS) + 4 (Fisher scoring)confint(dnam_model3)## 2.5 % 97.5 %
## (Intercept) -7.024514e+00 -6.217135e+00
## PM_3moPreSamp -1.713528e-02 2.458680e-02
## sexFemale -9.168874e-02 9.874514e-02
## age_dx -6.414744e-03 3.862837e-03
## smokeHxEver -1.161136e-01 5.402423e-02
## dich_RaceNon-White -1.728670e-01 1.848611e-01
## metromicropolitan -1.813470e-01 1.172606e-01
## metrorural -2.030742e-01 1.181609e-01
## (phi) 1.920354e+03 2.377551e+03No significant association between PM_3moPreSamp and prop_5mC in this model.
17.1.5 Continuous PM2.5 1mo Pre-Sample
dnam_model1 <- betareg(prop_5mC ~ PM_1moPreSamp, dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ PM_1moPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0726 -0.3971 -0.0639 0.2809 5.3385
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.722037 0.076019 -88.426 <2e-16 ***
## PM_1moPreSamp 0.002840 0.009623 0.295 0.768
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2127.3 114.9 18.51 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4369 on 3 Df
## Pseudo R-squared: 0.0002713
## Number of iterations: 613 (BFGS) + 3 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.8710315 -6.57304347
## PM_1moPreSamp -0.0160201 0.02170076
## (phi) 1901.9886325 2352.53440447No significant association between PM_1moPreSamp and prop_5mC in this model.
Partial model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ PM_1moPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ PM_1moPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1189 -0.3941 -0.0776 0.2718 5.3498
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.631128 0.202248 -32.787 <2e-16 ***
## PM_1moPreSamp 0.002846 0.009644 0.295 0.768
## sexFemale 0.015608 0.047845 0.326 0.744
## age_dx -0.001180 0.002620 -0.450 0.652
## smokeHxEver -0.021775 0.043234 -0.504 0.615
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2129 115 18.51 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4369 on 6 Df
## Pseudo R-squared: 0.002195
## Number of iterations: 254 (BFGS) + 3 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -7.027527e+00 -6.234729e+00
## PM_1moPreSamp -1.605698e-02 2.174809e-02
## sexFemale -7.816577e-02 1.093825e-01
## age_dx -6.314977e-03 3.954973e-03
## smokeHxEver -1.065119e-01 6.296159e-02
## (phi) 1.903729e+03 2.354673e+03No significant association between PM_1moPreSamp and prop_5mC in this model.
Complete model with age_dx, sex, smokeHx, race, urbanicity
dnam_model3 <- betareg(prop_5mC ~ PM_1moPreSamp + sex + age_dx + smokeHx + dich_Race + metro, data=dnam)
summary(dnam_model3)##
## Call:
## betareg(formula = prop_5mC ~ PM_1moPreSamp + sex + age_dx + smokeHx +
## dich_Race + metro, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1399 -0.3904 -0.0749 0.2745 5.3622
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.619314 0.203572 -32.516 <2e-16 ***
## PM_1moPreSamp 0.001641 0.009784 0.168 0.867
## sexFemale 0.015602 0.047948 0.325 0.745
## age_dx -0.001091 0.002625 -0.416 0.678
## smokeHxEver -0.024175 0.043324 -0.558 0.577
## dich_RaceNon-White 0.003114 0.091452 0.034 0.973
## metromicropolitan -0.039626 0.075655 -0.524 0.600
## metrorural -0.047253 0.081853 -0.577 0.564
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2130.1 115.2 18.5 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4363 on 9 Df
## Pseudo R-squared: 0.003964
## Number of iterations: 153 (BFGS) + 3 (Fisher scoring)confint(dnam_model3)## 2.5 % 97.5 %
## (Intercept) -7.018307e+00 -6.220321e+00
## PM_1moPreSamp -1.753538e-02 2.081713e-02
## sexFemale -7.837399e-02 1.095773e-01
## age_dx -6.236622e-03 4.053837e-03
## smokeHxEver -1.090879e-01 6.073764e-02
## dich_RaceNon-White -1.761295e-01 1.823570e-01
## metromicropolitan -1.879074e-01 1.086562e-01
## metrorural -2.076825e-01 1.131756e-01
## (phi) 1.904349e+03 2.355761e+03No significant association between PM_1moPreSamp and prop_5mC in this model.
17.2 SO4 Models
17.2.1 Continuous SO4 in 5yrs Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ SO4_5yrPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ SO4_5yrPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0892 -0.3952 -0.0703 0.2762 5.3447
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.76638 0.07432 -91.045 <2e-16 ***
## SO4_5yrPreSamp 0.04424 0.04876 0.907 0.364
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2142.4 115.3 18.57 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4401 on 3 Df
## Pseudo R-squared: 0.002578
## Number of iterations: 5000 (BFGS) + 3 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.91204137 -6.6207150
## SO4_5yrPreSamp -0.05132734 0.1398149
## (phi) 1916.34227006 2368.4671320No significant association between SO4_5yrPreSamp and prop_5mC in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ SO4_5yrPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ SO4_5yrPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1342 -0.4027 -0.0757 0.2774 5.3580
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.668598 0.199047 -33.503 <2e-16 ***
## SO4_5yrPreSamp 0.042210 0.048809 0.865 0.387
## sexFemale 0.015039 0.047654 0.316 0.752
## age_dx -0.001249 0.002599 -0.480 0.631
## smokeHxEver -0.020191 0.043070 -0.469 0.639
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2144.3 115.4 18.57 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4401 on 6 Df
## Pseudo R-squared: 0.004426
## Number of iterations: 365 (BFGS) + 4 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -7.058722e+00 -6.27847285
## SO4_5yrPreSamp -5.345490e-02 0.13787442
## sexFemale -7.836102e-02 0.10843943
## age_dx -6.342786e-03 0.00384573
## smokeHxEver -1.046065e-01 0.06422448
## (phi) 1.918030e+03 2370.54001805No significant association between SO4_5yrPreSamp and prop_5mC in this model.
17.2.2 Continuous SO4 in 1yrs Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ SO4_1yrPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ SO4_1yrPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1380 -0.3999 -0.0628 0.2688 5.3355
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.79894 0.07751 -87.713 <2e-16 ***
## SO4_1yrPreSamp 0.08866 0.06714 1.321 0.187
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2135.5 115.3 18.52 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4376 on 3 Df
## Pseudo R-squared: 0.005371
## Number of iterations: 1068 (BFGS) + 3 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.95086658 -6.6470194
## SO4_1yrPreSamp -0.04293048 0.2202559
## (phi) 1909.48632079 2361.4149580No significant association between SO4_1yrPreSamp and prop_5mC in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ SO4_1yrPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ SO4_1yrPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1842 -0.3928 -0.0668 0.2749 5.3485
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.700623 0.198852 -33.696 <2e-16 ***
## SO4_1yrPreSamp 0.087698 0.067163 1.306 0.192
## sexFemale 0.015546 0.047828 0.325 0.745
## age_dx -0.001285 0.002602 -0.494 0.621
## smokeHxEver -0.020307 0.043143 -0.471 0.638
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2137.4 115.4 18.52 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4377 on 6 Df
## Pseudo R-squared: 0.00734
## Number of iterations: 274 (BFGS) + 3 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -7.090366e+00 -6.310879e+00
## SO4_1yrPreSamp -4.393938e-02 2.193344e-01
## sexFemale -7.819503e-02 1.092870e-01
## age_dx -6.384944e-03 3.814719e-03
## smokeHxEver -1.048662e-01 6.425219e-02
## (phi) 1.911242e+03 2.363572e+03No significant association between SO4_1yrPreSamp and prop_5mC in this model.
17.2.3 Continuous SO4 in 6mo Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ SO4_6moPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ SO4_6moPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0987 -0.3929 -0.0751 0.2734 5.3065
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.76915 0.07342 -92.198 <2e-16 ***
## SO4_6moPreSamp 0.06060 0.06505 0.932 0.352
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2112.6 116.9 18.07 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4168 on 3 Df
## Pseudo R-squared: 0.003063
## Number of iterations: 5000 (BFGS) + 4 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.91304860 -6.6252482
## SO4_6moPreSamp -0.06690139 0.1880926
## (phi) 1883.39659609 2341.7071950No significant association between SO4_6moPreSamp and prop_5mC in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ SO4_6moPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ SO4_6moPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1645 -0.3944 -0.0696 0.2734 5.3213
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.659102 0.199519 -33.376 <2e-16 ***
## SO4_6moPreSamp 0.060058 0.065067 0.923 0.356
## sexFemale 0.003989 0.049412 0.081 0.936
## age_dx -0.001354 0.002645 -0.512 0.609
## smokeHxEver -0.027577 0.044334 -0.622 0.534
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2115 117 18.07 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4169 on 6 Df
## Pseudo R-squared: 0.005279
## Number of iterations: 307 (BFGS) + 4 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -7.050152e+00 -6.268051e+00
## SO4_6moPreSamp -6.747117e-02 1.875866e-01
## sexFemale -9.285614e-02 1.008334e-01
## age_dx -6.537529e-03 3.828911e-03
## smokeHxEver -1.144696e-01 5.931470e-02
## (phi) 1.885328e+03 2.344093e+03No significant association between SO4_6moPreSamp and prop_5mC in this model.
17.2.4 Continuous SO4 in 3mo Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ SO4_3moPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ SO4_3moPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0862 -0.3801 -0.0591 0.2660 5.0351
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.68446 0.07473 -89.453 <2e-16 ***
## SO4_3moPreSamp 0.03003 0.06580 0.456 0.648
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 1866.1 110.5 16.89 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 3619 on 3 Df
## Pseudo R-squared: 0.0008348
## Number of iterations: 1705 (BFGS) + 3 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.83092254 -6.538001
## SO4_3moPreSamp -0.09894427 0.159000
## (phi) 1649.49370731 2082.620766No significant association between SO4_3moPreSamp and prop_5mC in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ SO4_3moPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ SO4_3moPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1627 -0.3992 -0.0586 0.2692 5.0393
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.6197749 0.2124773 -31.155 <2e-16 ***
## SO4_3moPreSamp 0.0306121 0.0658279 0.465 0.642
## sexFemale -0.0094719 0.0543310 -0.174 0.862
## age_dx -0.0005727 0.0028849 -0.199 0.843
## smokeHxEver -0.0374484 0.0488809 -0.766 0.444
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 1868.1 110.6 16.89 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 3619 on 6 Df
## Pseudo R-squared: 0.003239
## Number of iterations: 305 (BFGS) + 4 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -7.036223e+00 -6.20332715
## SO4_3moPreSamp -9.840814e-02 0.15963240
## sexFemale -1.159586e-01 0.09701486
## age_dx -6.226886e-03 0.00508153
## smokeHxEver -1.332533e-01 0.05835647
## (phi) 1.651310e+03 2084.89644145No significant association between SO4_3moPreSamp and prop_5mC in this model.
17.2.5 Continuous SO4 in 1mo Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ SO4_1moPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ SO4_1moPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0340 -0.3976 -0.0658 0.2728 4.9454
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.646757 0.075024 -88.595 <2e-16 ***
## SO4_1moPreSamp 0.009376 0.066090 0.142 0.887
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 1787.7 107.6 16.62 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 3499 on 3 Df
## Pseudo R-squared: 8.369e-05
## Number of iterations: 465 (BFGS) + 3 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.7938014 -6.499712
## SO4_1moPreSamp -0.1201581 0.138911
## (phi) 1576.8984016 1998.570166No significant association between SO4_1moPreSamp and prop_5mC in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ SO4_1moPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ SO4_1moPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0669 -0.4024 -0.0671 0.2745 4.9472
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.5876885 0.2175829 -30.277 <2e-16 ***
## SO4_1moPreSamp 0.0099026 0.0661027 0.150 0.881
## sexFemale -0.0007889 0.0552074 -0.014 0.989
## age_dx -0.0005254 0.0029701 -0.177 0.860
## smokeHxEver -0.0365086 0.0504846 -0.723 0.470
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 1789.6 107.7 16.62 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 3499 on 6 Df
## Pseudo R-squared: 0.002302
## Number of iterations: 216 (BFGS) + 3 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -7.014143e+00 -6.161234e+00
## SO4_1moPreSamp -1.196563e-01 1.394616e-01
## sexFemale -1.089934e-01 1.074156e-01
## age_dx -6.346717e-03 5.296005e-03
## smokeHxEver -1.354567e-01 6.243944e-02
## (phi) 1.578524e+03 2.000615e+03No significant association between SO4_1moPreSamp and prop_5mC in this model.
17.3 NO3 Models
17.3.1 Continuous NO3 in 5yrs Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ NO3_5yrPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ NO3_5yrPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.3435 -0.4032 -0.0508 0.2943 5.4147
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.76826 0.04216 -160.545 <2e-16 ***
## NO3_5yrPreSamp 0.07214 0.03853 1.872 0.0612 .
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2150.6 115.8 18.58 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4402 on 3 Df
## Pseudo R-squared: 0.01097
## Number of iterations: 5000 (BFGS) + 5 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.850886e+00 -6.6856298
## NO3_5yrPreSamp -3.376669e-03 0.1476661
## (phi) 1.923705e+03 2377.5067887Nitrate is marginally associated with higher global DNAm in this base model.
Adjuted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ NO3_5yrPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ NO3_5yrPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.3222 -0.3964 -0.0593 0.2963 5.4232
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.686531 0.188612 -35.451 <2e-16 ***
## NO3_5yrPreSamp 0.071439 0.038593 1.851 0.0642 .
## sexFemale 0.017015 0.047579 0.358 0.7206
## age_dx -0.001049 0.002601 -0.403 0.6868
## smokeHxEver -0.020931 0.042964 -0.487 0.6261
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2152.5 115.9 18.58 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4403 on 6 Df
## Pseudo R-squared: 0.01275
## Number of iterations: 373 (BFGS) + 4 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -7.056204e+00 -6.316859e+00
## NO3_5yrPreSamp -4.200898e-03 1.470796e-01
## sexFemale -7.623892e-02 1.102688e-01
## age_dx -6.145934e-03 4.048761e-03
## smokeHxEver -1.051387e-01 6.327709e-02
## (phi) 1.925364e+03 2.379544e+03Nitrate is marginally associated with higher global DNAm in this base model.
17.3.2 Continuous NO3 in 1yrs Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ NO3_1yrPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ NO3_1yrPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.4427 -0.4082 -0.0509 0.2934 5.4263
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.77738 0.04117 -164.611 <2e-16 ***
## NO3_1yrPreSamp 0.10093 0.04508 2.239 0.0252 *
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2145.5 115.8 18.52 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4378 on 3 Df
## Pseudo R-squared: 0.01595
## Number of iterations: 1089 (BFGS) + 4 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.85807589 -6.6966847
## NO3_1yrPreSamp 0.01257504 0.1892856
## (phi) 1918.48789591 2372.4708814Higher nitrate is associated with higher global DNAm in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ NO3_1yrPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ NO3_1yrPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.4199 -0.3957 -0.0516 0.2941 5.4336
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.7022647 0.1890792 -35.447 <2e-16 ***
## NO3_1yrPreSamp 0.1001976 0.0451771 2.218 0.0266 *
## sexFemale 0.0184402 0.0477476 0.386 0.6993
## age_dx -0.0009692 0.0026074 -0.372 0.7101
## smokeHxEver -0.0198825 0.0430671 -0.462 0.6443
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2147.3 115.9 18.53 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4378 on 6 Df
## Pseudo R-squared: 0.01763
## Number of iterations: 501 (BFGS) + 5 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -7.072853e+00 -6.331676e+00
## NO3_1yrPreSamp 1.165209e-02 1.887430e-01
## sexFemale -7.514352e-02 1.120238e-01
## age_dx -6.079582e-03 4.141149e-03
## smokeHxEver -1.042924e-01 6.452748e-02
## (phi) 1.920082e+03 2.374430e+03Higher nitrate is associated with higher global DNAm in this model.
17.3.3 Continuous NO3 in 6mo Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ NO3_6moPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ NO3_6moPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1621 -0.3978 -0.0656 0.2902 5.3539
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.73425 0.03496 -192.612 <2e-16 ***
## NO3_6moPreSamp 0.03681 0.03184 1.156 0.248
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2114 117 18.07 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4169 on 3 Df
## Pseudo R-squared: 0.004742
## Number of iterations: 269 (BFGS) + 5 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.80277289 -6.66572146
## NO3_6moPreSamp -0.02560227 0.09921783
## (phi) 1884.73539756 2343.35973604No assoc of nitrate with global DNAm in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ NO3_6moPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ NO3_6moPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1943 -0.3965 -0.0704 0.2882 5.3698
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.623085 0.188999 -35.043 <2e-16 ***
## NO3_6moPreSamp 0.036051 0.031924 1.129 0.259
## sexFemale 0.004530 0.049428 0.092 0.927
## age_dx -0.001394 0.002649 -0.526 0.599
## smokeHxEver -0.025160 0.044374 -0.567 0.571
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2116.0 117.1 18.07 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4169 on 6 Df
## Pseudo R-squared: 0.006826
## Number of iterations: 547 (BFGS) + 6 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -6.993516e+00 -6.252654e+00
## NO3_6moPreSamp -2.651904e-02 9.862158e-02
## sexFemale -9.234657e-02 1.014075e-01
## age_dx -6.585967e-03 3.797384e-03
## smokeHxEver -1.121313e-01 6.181226e-02
## (phi) 1.886525e+03 2.345570e+03No assoc of nitrate with global DNAm in this model.
17.3.4 Continuous NO3 in 3mo Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ NO3_3moPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ NO3_3moPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0763 -0.3982 -0.0680 0.2768 5.0419
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.66682 0.03525 -189.143 <2e-16 ***
## NO3_3moPreSamp 0.01893 0.03149 0.601 0.548
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 1866.5 110.5 16.89 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 3619 on 3 Df
## Pseudo R-squared: 0.001384
## Number of iterations: 704 (BFGS) + 4 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.7359016 -6.597734
## NO3_3moPreSamp -0.0427905 0.080653
## (phi) 1649.9126823 2083.145562No association between nitrate and global DNAm in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ NO3_3moPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ NO3_3moPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1357 -0.4020 -0.0571 0.2715 5.0451
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.6075956 0.2041558 -32.365 <2e-16 ***
## NO3_3moPreSamp 0.0182192 0.0315548 0.577 0.564
## sexFemale -0.0084106 0.0543709 -0.155 0.877
## age_dx -0.0004864 0.0028890 -0.168 0.866
## smokeHxEver -0.0367234 0.0488811 -0.751 0.452
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 1868.5 110.6 16.89 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 3619 on 6 Df
## Pseudo R-squared: 0.003666
## Number of iterations: 164 (BFGS) + 4 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -7.007734e+00 -6.207458e+00
## NO3_3moPreSamp -4.362708e-02 8.006540e-02
## sexFemale -1.149755e-01 9.815440e-02
## age_dx -6.148821e-03 5.176041e-03
## smokeHxEver -1.325286e-01 5.908184e-02
## (phi) 1.651622e+03 2.085287e+03No association between nitrate and global DNAm in this model.
17.3.5 Continuous NO3 in 1mo Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ NO3_1moPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ NO3_1moPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0355 -0.4031 -0.0654 0.2738 4.9471
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.6369180 0.0367755 -180.471 <2e-16 ***
## NO3_1moPreSamp 0.0001452 0.0346114 0.004 0.997
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 1787.7 107.6 16.62 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 3499 on 3 Df
## Pseudo R-squared: 6.847e-08
## Number of iterations: 1906 (BFGS) + 2 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.70899672 -6.5648393
## NO3_1moPreSamp -0.06769194 0.0679824
## (phi) 1576.84017979 1998.4969771No association between nitrate and global DNAm in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ NO3_1moPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ NO3_1moPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0818 -0.4059 -0.0679 0.2752 4.9490
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.5784931 0.2096448 -31.379 <2e-16 ***
## NO3_1moPreSamp 0.0005675 0.0346469 0.016 0.987
## sexFemale -0.0009629 0.0552040 -0.017 0.986
## age_dx -0.0005114 0.0029732 -0.172 0.863
## smokeHxEver -0.0365340 0.0504934 -0.724 0.469
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 1789.5 107.7 16.62 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 3499 on 6 Df
## Pseudo R-squared: 0.002213
## Number of iterations: 125 (BFGS) + 3 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -6.989389e+00 -6.167597e+00
## NO3_1moPreSamp -6.733931e-02 6.847422e-02
## sexFemale -1.091607e-01 1.072349e-01
## age_dx -6.338706e-03 5.315905e-03
## smokeHxEver -1.354992e-01 6.243123e-02
## (phi) 1.578460e+03 2.000533e+03No association between nitrate and global DNAm in this model.
17.4 NH4 Models
17.4.1 Continuous NH4 in 5yrs Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ NH4_5yrPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ NH4_5yrPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1488 -0.3940 -0.0575 0.2971 5.3882
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.78685 0.05145 -131.908 <2e-16 ***
## NH4_5yrPreSamp 0.17700 0.09601 1.844 0.0652 .
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2150.2 115.7 18.58 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4402 on 3 Df
## Pseudo R-squared: 0.01036
## Number of iterations: 3623 (BFGS) + 3 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.8876896 -6.6860046
## NH4_5yrPreSamp -0.0111714 0.3651737
## (phi) 1923.3501515 2377.0720027Higher NH4 marginally associated with higher global DNAm in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ NH4_5yrPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ NH4_5yrPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1685 -0.4036 -0.0593 0.2770 5.3962
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.7127980 0.1929334 -34.793 <2e-16 ***
## NH4_5yrPreSamp 0.1715980 0.0963726 1.781 0.075 .
## sexFemale 0.0142767 0.0475909 0.300 0.764
## age_dx -0.0009185 0.0026048 -0.353 0.724
## smokeHxEver -0.0187735 0.0430170 -0.436 0.663
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2151.6 115.8 18.58 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4403 on 6 Df
## Pseudo R-squared: 0.01174
## Number of iterations: 150 (BFGS) + 2 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -7.090941e+00 -6.334655e+00
## NH4_5yrPreSamp -1.728887e-02 3.604849e-01
## sexFemale -7.899988e-02 1.075532e-01
## age_dx -6.023946e-03 4.186848e-03
## smokeHxEver -1.030853e-01 6.553827e-02
## (phi) 1.924606e+03 2.378614e+03Higher NH4 marginally associated with higher global DNAm in this model.
17.4.2 Continuous NH4 in 1yrs Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ NH4_1yrPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ NH4_1yrPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.2285 -0.3894 -0.0526 0.2861 5.3791
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.77129 0.04509 -150.18 <2e-16 ***
## NH4_1yrPreSamp 0.25222 0.13935 1.81 0.0703 .
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2140.0 115.5 18.52 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4377 on 3 Df
## Pseudo R-squared: 0.009959
## Number of iterations: 641 (BFGS) + 2 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.85965816 -6.6829198
## NH4_1yrPreSamp -0.02089795 0.5253479
## (phi) 1913.57989964 2366.4435174Higher NH4 marginally associated with higher global DNAm in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ NH4_1yrPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ NH4_1yrPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.2094 -0.3837 -0.0633 0.2862 5.3896
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.687778 0.189614 -35.271 <2e-16 ***
## NH4_1yrPreSamp 0.246605 0.139606 1.766 0.0773 .
## sexFemale 0.015851 0.047791 0.332 0.7401
## age_dx -0.001085 0.002607 -0.416 0.6772
## smokeHxEver -0.017962 0.043140 -0.416 0.6771
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2141.6 115.6 18.52 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4377 on 6 Df
## Pseudo R-squared: 0.01164
## Number of iterations: 268 (BFGS) + 3 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -7.059414e+00 -6.316142e+00
## NH4_1yrPreSamp -2.701741e-02 5.202272e-01
## sexFemale -7.781759e-02 1.095198e-01
## age_dx -6.194239e-03 4.024194e-03
## smokeHxEver -1.025148e-01 6.659066e-02
## (phi) 1.914987e+03 2.368172e+03Higher NH4 marginally associated with higher global DNAm in this model.
17.4.3 Continuous NH4 in 6mo Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ NH4_6moPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ NH4_6moPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0588 -0.3997 -0.0670 0.2814 5.3270
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.72360 0.03929 -171.111 <2e-16 ***
## NH4_6moPreSamp 0.06665 0.10977 0.607 0.544
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2110.9 116.8 18.07 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4168 on 3 Df
## Pseudo R-squared: 0.001321
## Number of iterations: 1439 (BFGS) + 4 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.8006102 -6.6465813
## NH4_6moPreSamp -0.1484991 0.2817968
## (phi) 1881.9369985 2339.9046836Higher NH4 not associated with global DNAm in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ NH4_6moPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ NH4_6moPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1161 -0.3919 -0.0731 0.2818 5.3425
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.611737 0.189560 -34.879 <2e-16 ***
## NH4_6moPreSamp 0.062263 0.110190 0.565 0.572
## sexFemale 0.003808 0.049437 0.077 0.939
## age_dx -0.001384 0.002649 -0.523 0.601
## smokeHxEver -0.026053 0.044430 -0.586 0.558
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2113.0 116.9 18.07 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4168 on 6 Df
## Pseudo R-squared: 0.00347
## Number of iterations: 345 (BFGS) + 4 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -6.983267e+00 -6.240207e+00
## NH4_6moPreSamp -1.537054e-01 2.782309e-01
## sexFemale -9.308685e-02 1.007023e-01
## age_dx -6.575586e-03 3.807315e-03
## smokeHxEver -1.131343e-01 6.102747e-02
## (phi) 1.883758e+03 2.342154e+03Higher NH4 not associated with global DNAm in this model.
17.4.4 Continuous NH4 in 3mo Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ NH4_3moPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ NH4_3moPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0794 -0.4001 -0.0674 0.2759 5.0393
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.66813 0.03927 -169.800 <2e-16 ***
## NH4_3moPreSamp 0.05752 0.10835 0.531 0.596
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 1866.3 110.5 16.89 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 3619 on 3 Df
## Pseudo R-squared: 0.001082
## Number of iterations: 1019 (BFGS) + 2 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.745101 -6.5911634
## NH4_3moPreSamp -0.154847 0.2698831
## (phi) 1649.688564 2082.8648333Higher NH4 not associated with global DNAm in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ NH4_3moPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ NH4_3moPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1502 -0.4042 -0.0573 0.2652 5.0430
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.6084645 0.2051993 -32.205 <2e-16 ***
## NH4_3moPreSamp 0.0533942 0.1086784 0.491 0.623
## sexFemale -0.0088638 0.0543484 -0.163 0.870
## age_dx -0.0004866 0.0028885 -0.168 0.866
## smokeHxEver -0.0362977 0.0488935 -0.742 0.458
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 1868.2 110.6 16.89 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 3619 on 6 Df
## Pseudo R-squared: 0.003351
## Number of iterations: 190 (BFGS) + 4 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -7.010648e+00 -6.206281e+00
## NH4_3moPreSamp -1.596116e-01 2.664000e-01
## sexFemale -1.153848e-01 9.765716e-02
## age_dx -6.147989e-03 5.174696e-03
## smokeHxEver -1.321273e-01 5.953186e-02
## (phi) 1.651356e+03 2.084953e+03Higher NH4 not associated with global DNAm in this model.
17.4.5 Continuous NH4 in 1mo Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ NH4_1moPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ NH4_1moPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0354 -0.4029 -0.0651 0.2740 4.9468
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.637143 0.039647 -167.408 <2e-16 ***
## NH4_1moPreSamp 0.001264 0.111966 0.011 0.991
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 1787.7 107.6 16.62 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 3499 on 3 Df
## Pseudo R-squared: 5.137e-07
## Number of iterations: 270 (BFGS) + 3 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.7148489 -6.5594372
## NH4_1moPreSamp -0.2181853 0.2207123
## (phi) 1576.8404943 1998.4973724Higher NH4 not associated with global DNAm in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ NH4_1moPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ NH4_1moPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0807 -0.4062 -0.0683 0.2747 4.9488
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.5777754 0.2101242 -31.304 <2e-16 ***
## NH4_1moPreSamp -0.0004349 0.1121467 -0.004 0.997
## sexFemale -0.0009723 0.0552015 -0.018 0.986
## age_dx -0.0005143 0.0029722 -0.173 0.863
## smokeHxEver -0.0365109 0.0504852 -0.723 0.470
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 1789.5 107.7 16.62 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 3499 on 6 Df
## Pseudo R-squared: 0.002212
## Number of iterations: 203 (BFGS) + 3 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -6.989611e+00 -6.165939e+00
## NH4_1moPreSamp -2.202384e-01 2.193686e-01
## sexFemale -1.091653e-01 1.072207e-01
## age_dx -6.339663e-03 5.311005e-03
## smokeHxEver -1.354600e-01 6.243819e-02
## (phi) 1.578459e+03 2.000532e+03Higher NH4 not associated with global DNAm in this model.
17.5 BC Models
17.5.1 Continuous BC in 5yrs Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ BC_5yrPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ BC_5yrPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0762 -0.3996 -0.0655 0.2816 5.3468
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.73713 0.06805 -99.002 <2e-16 ***
## BC_5yrPreSamp 0.05666 0.10426 0.543 0.587
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2140.8 115.3 18.57 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4401 on 3 Df
## Pseudo R-squared: 0.0008914
## Number of iterations: 417 (BFGS) + 3 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.8705094 -6.6037560
## BC_5yrPreSamp -0.1476886 0.2610144
## (phi) 1914.8608419 2366.6480173No significant association between BC_5yrPreSamp and prop_5mC in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ BC_5yrPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ BC_5yrPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1240 -0.4023 -0.0760 0.2732 5.3592
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.637310 0.194808 -34.071 <2e-16 ***
## BC_5yrPreSamp 0.059213 0.104343 0.567 0.570
## sexFemale 0.016935 0.047714 0.355 0.723
## age_dx -0.001340 0.002599 -0.516 0.606
## smokeHxEver -0.021330 0.043039 -0.496 0.620
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2142.9 115.4 18.57 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4401 on 6 Df
## Pseudo R-squared: 0.003019
## Number of iterations: 303 (BFGS) + 3 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -7.019126e+00 -6.255494e+00
## BC_5yrPreSamp -1.452962e-01 2.637227e-01
## sexFemale -7.658207e-02 1.104526e-01
## age_dx -6.433826e-03 3.753028e-03
## smokeHxEver -1.056842e-01 6.302484e-02
## (phi) 1.916831e+03 2.369067e+03No significant association between BC_5yrPreSamp and prop_5mC in this model.
17.5.2 Continuous BC in 1yrs Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ BC_1yrPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ BC_1yrPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0738 -0.3922 -0.0673 0.2754 5.3467
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.704222 0.073541 -91.16 <2e-16 ***
## BC_1yrPreSamp 0.004687 0.117743 0.04 0.968
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2130 115 18.52 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4375 on 3 Df
## Pseudo R-squared: 5.032e-06
## Number of iterations: 634 (BFGS) + 4 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.8483601 -6.5600841
## BC_1yrPreSamp -0.2260847 0.2354577
## (phi) 1904.6991624 2355.5331992No significant association between BC_1yrPreSamp and prop_5mC in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ BC_1yrPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ BC_1yrPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1226 -0.3988 -0.0727 0.2705 5.3595
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.603723 0.196535 -33.601 <2e-16 ***
## BC_1yrPreSamp 0.005677 0.117746 0.048 0.962
## sexFemale 0.015400 0.047872 0.322 0.748
## age_dx -0.001330 0.002606 -0.510 0.610
## smokeHxEver -0.021433 0.043186 -0.496 0.620
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2132.2 115.1 18.52 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4376 on 6 Df
## Pseudo R-squared: 0.002069
## Number of iterations: 216 (BFGS) + 3 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -6.988925e+00 -6.218520e+00
## BC_1yrPreSamp -2.251022e-01 2.364556e-01
## sexFemale -7.842704e-02 1.092270e-01
## age_dx -6.438391e-03 3.778581e-03
## smokeHxEver -1.060762e-01 6.320951e-02
## (phi) 1.906566e+03 2.357827e+03No significant association between BC_1yrPreSamp and prop_5mC in this model.
17.5.3 Continuous BC in 6mo Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ BC_6moPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ BC_6moPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0733 -0.3898 -0.0622 0.2730 5.3295
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.67222 0.06107 -109.247 <2e-16 ***
## BC_6moPreSamp -0.05189 0.09080 -0.571 0.568
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2110.8 116.8 18.07 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4168 on 3 Df
## Pseudo R-squared: 0.001181
## Number of iterations: 417 (BFGS) + 4 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.7919213 -6.5525135
## BC_6moPreSamp -0.2298576 0.1260793
## (phi) 1881.8241353 2339.7654183No significant association between BC_6moPreSamp and prop_5mC in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ BC_6moPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ BC_6moPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0892 -0.3864 -0.0644 0.2765 5.3422
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.568496 0.193328 -33.976 <2e-16 ***
## BC_6moPreSamp -0.049928 0.091005 -0.549 0.583
## sexFemale 0.002725 0.049446 0.055 0.956
## age_dx -0.001273 0.002652 -0.480 0.631
## smokeHxEver -0.028711 0.044361 -0.647 0.517
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2112.9 116.9 18.07 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4168 on 6 Df
## Pseudo R-squared: 0.003391
## Number of iterations: 242 (BFGS) + 3 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -6.94741302 -6.189580e+00
## BC_6moPreSamp -0.22829472 1.284384e-01
## sexFemale -0.09418784 9.963843e-02
## age_dx -0.00647144 3.925068e-03
## smokeHxEver -0.11565760 5.823464e-02
## (phi) 1883.72066410 2.342107e+03No significant association between BC_6moPreSamp and prop_5mC in this model.
17.5.4 Continuous BC in 3mo Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ BC_3moPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ BC_3moPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1362 -0.3898 -0.0609 0.2757 5.0379
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.69549 0.06489 -103.181 <2e-16 ***
## BC_3moPreSamp 0.07272 0.10040 0.724 0.469
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 1867.1 110.6 16.89 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 3619 on 3 Df
## Pseudo R-squared: 0.002054
## Number of iterations: 1264 (BFGS) + 3 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.8226739 -6.5683068
## BC_3moPreSamp -0.1240575 0.2694969
## (phi) 1650.3998993 2083.7559858No significant association between BC_3moPreSamp and prop_5mC in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ BC_3moPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ BC_3moPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.2169 -0.3994 -0.0541 0.2852 5.0416
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.6333093 0.2109832 -31.440 <2e-16 ***
## BC_3moPreSamp 0.0747411 0.1004006 0.744 0.457
## sexFemale -0.0099368 0.0543158 -0.183 0.855
## age_dx -0.0005352 0.0028855 -0.185 0.853
## smokeHxEver -0.0382629 0.0488702 -0.783 0.434
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 1869.2 110.7 16.89 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 3619 on 6 Df
## Pseudo R-squared: 0.004468
## Number of iterations: 242 (BFGS) + 3 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -7.046829e+00 -6.219790e+00
## BC_3moPreSamp -1.220405e-01 2.715228e-01
## sexFemale -1.163939e-01 9.652030e-02
## age_dx -6.190719e-03 5.120365e-03
## smokeHxEver -1.340468e-01 5.752102e-02
## (phi) 1.652287e+03 2.086121e+03No significant association between BC_3moPreSamp and prop_5mC in this model.
17.5.5 Continuous BC in 1mo Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ BC_1moPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ BC_1moPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0184 -0.4017 -0.0608 0.2672 4.9455
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.67446 0.06353 -105.061 <2e-16 ***
## BC_1moPreSamp 0.06410 0.09790 0.655 0.513
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 1789.0 107.6 16.62 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 3499 on 3 Df
## Pseudo R-squared: 0.001655
## Number of iterations: 1083 (BFGS) + 3 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.7989713 -6.5499406
## BC_1moPreSamp -0.1277916 0.2559862
## (phi) 1578.0419781 2000.0078659No significant association between BC_1moPreSamp and prop_5mC in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ BC_1moPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ BC_1moPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0378 -0.4050 -0.0573 0.2834 4.9465
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.6174022 0.2156676 -30.683 <2e-16 ***
## BC_1moPreSamp 0.0672957 0.0979361 0.687 0.492
## sexFemale -0.0002692 0.0551875 -0.005 0.996
## age_dx -0.0005044 0.0029693 -0.170 0.865
## smokeHxEver -0.0378730 0.0504746 -0.750 0.453
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 1791.0 107.8 16.62 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 3500 on 6 Df
## Pseudo R-squared: 0.003916
## Number of iterations: 196 (BFGS) + 4 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -7.040103e+00 -6.194701e+00
## BC_1moPreSamp -1.246556e-01 2.592469e-01
## sexFemale -1.084347e-01 1.078964e-01
## age_dx -6.324207e-03 5.315352e-03
## smokeHxEver -1.368014e-01 6.105546e-02
## (phi) 1.579796e+03 2.002213e+03No significant association between BC_1moPreSamp and prop_5mC in this model.
17.6 OM Models
17.6.1 Continuous OM in 5yrs Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ OM_5yrPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ OM_5yrPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0751 -0.3923 -0.0665 0.2761 5.3599
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.705251 0.070833 -94.663 <2e-16 ***
## OM_5yrPreSamp 0.001038 0.023716 0.044 0.965
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2139.9 115.2 18.57 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4401 on 3 Df
## Pseudo R-squared: 5.956e-06
## Number of iterations: 620 (BFGS) + 3 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.84408095 -6.5664218
## OM_5yrPreSamp -0.04544473 0.0475205
## (phi) 1914.06415810 2365.6697541No significant association between OM_5yrPreSamp and prop_5mC in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ OM_5yrPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ OM_5yrPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1242 -0.3995 -0.0772 0.2704 5.3726
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.605964 0.196330 -33.647 <2e-16 ***
## OM_5yrPreSamp 0.001672 0.023746 0.070 0.944
## sexFemale 0.015779 0.047752 0.330 0.741
## age_dx -0.001330 0.002600 -0.512 0.609
## smokeHxEver -0.021521 0.043042 -0.500 0.617
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2142.0 115.3 18.57 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4401 on 6 Df
## Pseudo R-squared: 0.0021
## Number of iterations: 112 (BFGS) + 3 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -6.990764e+00 -6.221163e+00
## OM_5yrPreSamp -4.486938e-02 4.821437e-02
## sexFemale -7.781207e-02 1.093708e-01
## age_dx -6.425473e-03 3.765746e-03
## smokeHxEver -1.058823e-01 6.284053e-02
## (phi) 1.915965e+03 2.368004e+03No significant association between OM_5yrPreSamp and prop_5mC in this model.
17.6.2 Continuous OM in 1yrs Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ OM_1yrPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ OM_1yrPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0732 -0.3830 -0.0651 0.2788 5.3650
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.66790 0.06916 -96.413 <2e-16 ***
## OM_1yrPreSamp -0.01111 0.02168 -0.513 0.608
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2130.9 115.1 18.52 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4376 on 3 Df
## Pseudo R-squared: 0.0008603
## Number of iterations: 88 (BFGS) + 3 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.80345291 -6.5323517
## OM_1yrPreSamp -0.05361151 0.0313841
## (phi) 1905.43479829 2356.4369761No significant association between OM_1yrPreSamp and prop_5mC in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ OM_1yrPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ OM_1yrPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1018 -0.3876 -0.0690 0.2764 5.3784
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.567592 0.196053 -33.499 <2e-16 ***
## OM_1yrPreSamp -0.010790 0.021721 -0.497 0.619
## sexFemale 0.014044 0.047938 0.293 0.770
## age_dx -0.001325 0.002607 -0.508 0.611
## smokeHxEver -0.021765 0.043166 -0.504 0.614
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2133.0 115.2 18.52 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4376 on 6 Df
## Pseudo R-squared: 0.002871
## Number of iterations: 346 (BFGS) + 3 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -6.951848e+00 -6.183336e+00
## OM_1yrPreSamp -5.336236e-02 3.178289e-02
## sexFemale -7.991235e-02 1.080005e-01
## age_dx -6.433739e-03 3.784272e-03
## smokeHxEver -1.063690e-01 6.283806e-02
## (phi) 1.907254e+03 2.358672e+03No significant association between OM_1yrPreSamp and prop_5mC in this model.
17.6.3 Continuous OM in 6mo Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ OM_6moPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ OM_6moPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0752 -0.3761 -0.0529 0.2772 5.3540
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.64928 0.05982 -111.2 <2e-16 ***
## OM_6moPreSamp -0.01760 0.01761 -1.0 0.318
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2113.0 116.9 18.07 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4168 on 3 Df
## Pseudo R-squared: 0.003534
## Number of iterations: 740 (BFGS) + 4 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.76652082 -6.53203180
## OM_6moPreSamp -0.05210588 0.01690988
## (phi) 1883.79643121 2342.20111613No significant association between OM_6moPreSamp and prop_5mC in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ OM_6moPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ OM_6moPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0727 -0.3803 -0.0619 0.2743 5.3683
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.543505 0.194078 -33.716 <2e-16 ***
## OM_6moPreSamp -0.017468 0.017669 -0.989 0.323
## sexFemale -0.000434 0.049536 -0.009 0.993
## age_dx -0.001274 0.002650 -0.481 0.631
## smokeHxEver -0.029316 0.044326 -0.661 0.508
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2115.1 117.1 18.07 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4169 on 6 Df
## Pseudo R-squared: 0.00567
## Number of iterations: 242 (BFGS) + 3 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -6.92389028 -6.163120e+00
## OM_6moPreSamp -0.05209857 1.716263e-02
## sexFemale -0.09752197 9.665405e-02
## age_dx -0.00646855 3.920306e-03
## smokeHxEver -0.11619313 5.756049e-02
## (phi) 1885.70778639 2.344562e+03No significant association between OM_6moPreSamp and prop_5mC in this model.
17.6.4 Continuous OM in 3mo Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ OM_3moPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ OM_3moPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0648 -0.3936 -0.0608 0.2796 5.0418
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.6553839 0.0621073 -107.159 <2e-16 ***
## OM_3moPreSamp 0.0009425 0.0186360 0.051 0.96
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 1865.4 110.5 16.89 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 3619 on 3 Df
## Pseudo R-squared: 1.05e-05
## Number of iterations: 263 (BFGS) + 3 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.77711203 -6.53365578
## OM_3moPreSamp -0.03558341 0.03746848
## (phi) 1648.89342679 2081.86878297No significant association between OM_3moPreSamp and prop_5mC in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ OM_3moPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ OM_3moPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1402 -0.3956 -0.0626 0.2714 5.0461
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.592241 0.210304 -31.346 <2e-16 ***
## OM_3moPreSamp 0.001130 0.018654 0.061 0.952
## sexFemale -0.009525 0.054375 -0.175 0.861
## age_dx -0.000550 0.002887 -0.191 0.849
## smokeHxEver -0.037345 0.048899 -0.764 0.445
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 1867.4 110.6 16.89 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 3619 on 6 Df
## Pseudo R-squared: 0.002389
## Number of iterations: 164 (BFGS) + 3 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -7.004429e+00 -6.180054e+00
## OM_3moPreSamp -3.543155e-02 3.769071e-02
## sexFemale -1.160969e-01 9.704770e-02
## age_dx -6.208551e-03 5.108466e-03
## smokeHxEver -1.331857e-01 5.849539e-02
## (phi) 1.650690e+03 2.084119e+03No significant association between OM_3moPreSamp and prop_5mC in this model.
17.6.5 Continuous OM in 1mo Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ OM_1moPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ OM_1moPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0359 -0.4023 -0.0649 0.2739 4.9475
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.6360060 0.0602069 -110.220 <2e-16 ***
## OM_1moPreSamp -0.0002649 0.0177554 -0.015 0.988
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 1787.7 107.6 16.62 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 3499 on 3 Df
## Pseudo R-squared: 9.544e-07
## Number of iterations: 270 (BFGS) + 3 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.75400939 -6.51800259
## OM_1moPreSamp -0.03506479 0.03453505
## (phi) 1576.84078287 1998.49773516No significant association between OM_1moPreSamp and prop_5mC in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ OM_1moPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ OM_1moPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0815 -0.4056 -0.0684 0.2754 4.9492
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.5786296 0.2144246 -30.680 <2e-16 ***
## OM_1moPreSamp 0.0002388 0.0177909 0.013 0.989
## sexFemale -0.0009271 0.0552865 -0.017 0.987
## age_dx -0.0005141 0.0029707 -0.173 0.863
## smokeHxEver -0.0365293 0.0505062 -0.723 0.470
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 1789.5 107.7 16.62 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 3499 on 6 Df
## Pseudo R-squared: 0.002213
## Number of iterations: 137 (BFGS) + 3 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -6.99889405 -6.158365e+00
## OM_1moPreSamp -0.03463082 3.510837e-02
## sexFemale -0.10928656 1.074324e-01
## age_dx -0.00633657 5.308324e-03
## smokeHxEver -0.13551959 6.246097e-02
## (phi) 1578.45965171 2.000533e+03No significant association between OM_1moPreSamp and prop_5mC in this model.
17.7 SS Models
17.7.1 Continuous SS in 5yrs Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ SS_5yrPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ SS_5yrPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0720 -0.3899 -0.0578 0.2834 5.3641
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.69654 0.02985 -224.373 <2e-16 ***
## SS_5yrPreSamp -0.01611 0.05450 -0.296 0.768
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2140.1 115.2 18.57 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4401 on 3 Df
## Pseudo R-squared: 0.0002811
## Number of iterations: 1089 (BFGS) + 3 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.7550382 -6.63804567
## SS_5yrPreSamp -0.1229363 0.09071838
## (phi) 1914.3040988 2365.96438065No significant association between SS_5yrPreSamp and prop_5mC in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ SS_5yrPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ SS_5yrPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1199 -0.4043 -0.0707 0.2717 5.3763
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.598356 0.183839 -35.892 <2e-16 ***
## SS_5yrPreSamp -0.013103 0.054635 -0.240 0.810
## sexFemale 0.015320 0.047691 0.321 0.748
## age_dx -0.001307 0.002604 -0.502 0.616
## smokeHxEver -0.020985 0.043096 -0.487 0.626
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2142.2 115.3 18.57 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4401 on 6 Df
## Pseudo R-squared: 0.002256
## Number of iterations: 242 (BFGS) + 4 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -6.958675e+00 -6.238037e+00
## SS_5yrPreSamp -1.201858e-01 9.397972e-02
## sexFemale -7.815213e-02 1.087927e-01
## age_dx -6.409553e-03 3.796226e-03
## smokeHxEver -1.054509e-01 6.348041e-02
## (phi) 1.916116e+03 2.368189e+03No significant association between SS_5yrPreSamp and prop_5mC in this model.
17.7.2 Continuous SS in 1yrs Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ SS_1yrPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ SS_1yrPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0718 -0.3909 -0.0585 0.2850 5.3497
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.697963 0.028850 -232.166 <2e-16 ***
## SS_1yrPreSamp -0.008935 0.045803 -0.195 0.845
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2130 115 18.52 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4375 on 3 Df
## Pseudo R-squared: 0.000129
## Number of iterations: 2391 (BFGS) + 3 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.75450808 -6.64141866
## SS_1yrPreSamp -0.09870806 0.08083783
## (phi) 1904.80359011 2355.66149111No significant association between SS_1yrPreSamp and prop_5mC in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ SS_1yrPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ SS_1yrPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1197 -0.4005 -0.0724 0.2704 5.3623
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.598585 0.184376 -35.789 <2e-16 ***
## SS_1yrPreSamp -0.006818 0.045881 -0.149 0.882
## sexFemale 0.015233 0.047892 0.318 0.750
## age_dx -0.001319 0.002608 -0.506 0.613
## smokeHxEver -0.021110 0.043237 -0.488 0.625
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2132.3 115.1 18.52 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4376 on 6 Df
## Pseudo R-squared: 0.002132
## Number of iterations: 320 (BFGS) + 3 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -6.959956e+00 -6.237214e+00
## SS_1yrPreSamp -9.674419e-02 8.310787e-02
## sexFemale -7.863385e-02 1.090989e-01
## age_dx -6.430609e-03 3.791821e-03
## smokeHxEver -1.058518e-01 6.363260e-02
## (phi) 1.906624e+03 2.357898e+03No significant association between SS_1yrPreSamp and prop_5mC in this model.
17.7.3 Continuous SS in 6mo Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ SS_6moPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ SS_6moPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1467 -0.3920 -0.0622 0.2813 5.3081
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.71625 0.02873 -233.751 <2e-16 ***
## SS_6moPreSamp 0.03040 0.04273 0.711 0.477
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2111.3 116.9 18.07 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4168 on 3 Df
## Pseudo R-squared: 0.001616
## Number of iterations: 3343 (BFGS) + 3 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.77256322 -6.6599338
## SS_6moPreSamp -0.05335769 0.1141513
## (phi) 1882.26208737 2340.3062579No significant association between SS_6moPreSamp and prop_5mC in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ SS_6moPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ SS_6moPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.2824 -0.3913 -0.0672 0.2802 5.3216
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.604437 0.187505 -35.223 <2e-16 ***
## SS_6moPreSamp 0.032491 0.042805 0.759 0.448
## sexFemale 0.004555 0.049435 0.092 0.927
## age_dx -0.001379 0.002647 -0.521 0.602
## smokeHxEver -0.030107 0.044404 -0.678 0.498
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2114 117 18.07 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4168 on 6 Df
## Pseudo R-squared: 0.004115
## Number of iterations: 221 (BFGS) + 2 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -6.971940e+00 -6.236934e+00
## SS_6moPreSamp -5.140560e-02 1.163868e-01
## sexFemale -9.233491e-02 1.014454e-01
## age_dx -6.567051e-03 3.809361e-03
## smokeHxEver -1.171359e-01 5.692263e-02
## (phi) 1.884436e+03 2.342990e+03No significant association between SS_6moPreSamp and prop_5mC in this model.
17.7.4 Continuous SS in 3mo Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ SS_3moPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ SS_3moPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0509 -0.3861 -0.0626 0.2710 5.0370
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.65866 0.03094 -215.183 <2e-16 ***
## SS_3moPreSamp 0.01610 0.04427 0.364 0.716
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 1865.8 110.5 16.89 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 3619 on 3 Df
## Pseudo R-squared: 0.0004941
## Number of iterations: 1096 (BFGS) + 3 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.71930994 -6.5980108
## SS_3moPreSamp -0.07066867 0.1028688
## (phi) 1649.25840942 2082.3260149No significant association between SS_3moPreSamp and prop_5mC in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ SS_3moPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ SS_3moPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1411 -0.3960 -0.0554 0.2635 5.0402
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.5932796 0.2021169 -32.621 <2e-16 ***
## SS_3moPreSamp 0.0187923 0.0443956 0.423 0.672
## sexFemale -0.0095203 0.0543244 -0.175 0.861
## age_dx -0.0005766 0.0028861 -0.200 0.842
## smokeHxEver -0.0387379 0.0490000 -0.791 0.429
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 1868.0 110.6 16.89 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 3619 on 6 Df
## Pseudo R-squared: 0.003035
## Number of iterations: 183 (BFGS) + 3 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -6.989421e+00 -6.197138e+00
## SS_3moPreSamp -6.822136e-02 1.058061e-01
## sexFemale -1.159941e-01 9.695354e-02
## age_dx -6.233257e-03 5.080085e-03
## smokeHxEver -1.347763e-01 5.730040e-02
## (phi) 1.651183e+03 2.084737e+03No significant association between SS_3moPreSamp and prop_5mC in this model.
17.7.5 Continuous SS in 1mo Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ SS_1moPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ SS_1moPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0353 -0.4032 -0.0662 0.2776 4.9484
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.635967 0.031196 -212.721 <2e-16 ***
## SS_1moPreSamp -0.002166 0.041731 -0.052 0.959
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 1787.7 107.6 16.62 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 3499 on 3 Df
## Pseudo R-squared: 1.061e-05
## Number of iterations: 718 (BFGS) + 3 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.69710948 -6.57482463
## SS_1moPreSamp -0.08395841 0.07962586
## (phi) 1576.84776623 1998.50651478No significant association between SS_1moPreSamp and prop_5mC in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ SS_1moPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ SS_1moPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0840 -0.4058 -0.0683 0.2750 4.9495
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.578e+00 2.072e-01 -31.745 <2e-16 ***
## SS_1moPreSamp 1.913e-05 4.184e-02 0.000 1.000
## sexFemale -9.694e-04 5.521e-02 -0.018 0.986
## age_dx -5.139e-04 2.971e-03 -0.173 0.863
## smokeHxEver -3.651e-02 5.058e-02 -0.722 0.470
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 1789.5 107.7 16.62 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 3499 on 6 Df
## Pseudo R-squared: 0.002212
## Number of iterations: 163 (BFGS) + 2 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -6.984053e+00 -6.171799e+00
## SS_1moPreSamp -8.197974e-02 8.201800e-02
## sexFemale -1.091700e-01 1.072311e-01
## age_dx -6.337487e-03 5.309645e-03
## smokeHxEver -1.356441e-01 6.262374e-02
## (phi) 1.578459e+03 2.000532e+03No significant association between SS_1moPreSamp and prop_5mC in this model.
17.8 Soil Models
17.8.1 Continuous Soil in 5yrs Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ Soil_5yrPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ Soil_5yrPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0549 -0.3867 -0.0573 0.2892 5.3688
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.68287 0.04009 -166.679 <2e-16 ***
## Soil_5yrPreSamp -0.03231 0.05522 -0.585 0.559
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2140.9 115.3 18.57 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4401 on 3 Df
## Pseudo R-squared: 0.001072
## Number of iterations: 879 (BFGS) + 3 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.7614568 -6.60429057
## Soil_5yrPreSamp -0.1405345 0.07592242
## (phi) 1915.0092460 2366.83027988No significant association between Soil_5yrPreSamp and prop_5mC in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ Soil_5yrPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ Soil_5yrPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1199 -0.3929 -0.0731 0.2755 5.3806
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.588855 0.185056 -35.605 <2e-16 ***
## Soil_5yrPreSamp -0.029329 0.055365 -0.530 0.596
## sexFemale 0.015518 0.047661 0.326 0.745
## age_dx -0.001264 0.002604 -0.485 0.627
## smokeHxEver -0.020285 0.043115 -0.470 0.638
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2142.8 115.4 18.57 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4401 on 6 Df
## Pseudo R-squared: 0.002947
## Number of iterations: 255 (BFGS) + 3 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -6.951558e+00 -6.226153e+00
## Soil_5yrPreSamp -1.378416e-01 7.918383e-02
## sexFemale -7.789655e-02 1.089322e-01
## age_dx -6.366476e-03 3.839217e-03
## smokeHxEver -1.047890e-01 6.421995e-02
## (phi) 1.916735e+03 2.368949e+03No significant association between Soil_5yrPreSamp and prop_5mC in this model.
17.8.2 Continuous Soil in 1yrs Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ Soil_1yrPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ Soil_1yrPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0661 -0.3868 -0.0650 0.2811 5.3484
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.696799 0.041699 -160.599 <2e-16 ***
## Soil_1yrPreSamp -0.007922 0.059754 -0.133 0.895
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2130 115 18.52 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4375 on 3 Df
## Pseudo R-squared: 5.759e-05
## Number of iterations: 578 (BFGS) + 5 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.7785277 -6.6150711
## Soil_1yrPreSamp -0.1250372 0.1091924
## (phi) 1904.7442495 2355.5885911No significant association between Soil_1yrPreSamp and prop_5mC in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ Soil_1yrPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ Soil_1yrPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1157 -0.3983 -0.0676 0.2707 5.3610
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.597476 0.186468 -35.381 <2e-16 ***
## Soil_1yrPreSamp -0.006170 0.059746 -0.103 0.918
## sexFemale 0.015352 0.047873 0.321 0.748
## age_dx -0.001320 0.002607 -0.506 0.613
## smokeHxEver -0.021368 0.043188 -0.495 0.621
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2132.2 115.1 18.52 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4376 on 6 Df
## Pseudo R-squared: 0.0021
## Number of iterations: 294 (BFGS) + 3 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -6.962946e+00 -6.232005e+00
## Soil_1yrPreSamp -1.232706e-01 1.109301e-01
## sexFemale -7.847653e-02 1.091805e-01
## age_dx -6.429905e-03 3.790734e-03
## smokeHxEver -1.060151e-01 6.327939e-02
## (phi) 1.906590e+03 2.357856e+03No significant association between Soil_1yrPreSamp and prop_5mC in this model.
17.8.3 Continuous Soil in 6mo Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ Soil_6moPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ Soil_6moPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0559 -0.3802 -0.0641 0.2780 5.3166
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.701518 0.038543 -173.872 <2e-16 ***
## Soil_6moPreSamp -0.005203 0.053030 -0.098 0.922
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2109.7 116.8 18.07 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4168 on 3 Df
## Pseudo R-squared: 3.52e-05
## Number of iterations: 1019 (BFGS) + 3 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.7770611 -6.62597566
## Soil_6moPreSamp -0.1091401 0.09873448
## (phi) 1880.8874972 2338.60883090No significant association between Soil_6moPreSamp and prop_5mC in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ Soil_6moPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ Soil_6moPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1220 -0.3952 -0.0687 0.2738 5.3309
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.591913 0.189159 -34.849 <2e-16 ***
## Soil_6moPreSamp -0.003896 0.053067 -0.073 0.941
## sexFemale 0.003649 0.049457 0.074 0.941
## age_dx -0.001361 0.002649 -0.514 0.607
## smokeHxEver -0.028146 0.044359 -0.634 0.526
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 2111.9 116.9 18.07 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 4168 on 6 Df
## Pseudo R-squared: 0.002283
## Number of iterations: 196 (BFGS) + 2 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -6.962658e+00 -6.221168e+00
## Soil_6moPreSamp -1.079061e-01 1.001131e-01
## sexFemale -9.328536e-02 1.005829e-01
## age_dx -6.552341e-03 3.830855e-03
## smokeHxEver -1.150878e-01 5.879679e-02
## (phi) 1.882855e+03 2.341039e+03No significant association between Soil_6moPreSamp and prop_5mC in this model.
17.8.4 Continuous Soil in 3mo Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ Soil_3moPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ Soil_3moPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0520 -0.3909 -0.0637 0.2718 5.0391
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.66071 0.03806 -175.008 <2e-16 ***
## Soil_3moPreSamp 0.01380 0.04695 0.294 0.769
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 1865.7 110.5 16.89 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 3619 on 3 Df
## Pseudo R-squared: 0.0003832
## Number of iterations: 1145 (BFGS) + 2 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.7353016 -6.5861115
## Soil_3moPreSamp -0.0782205 0.1058194
## (phi) 1649.1480175 2082.1876444No significant association between Soil_3moPreSamp and prop_5mC in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ Soil_3moPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ Soil_3moPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.1258 -0.3985 -0.0612 0.2637 5.0427
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.5988793 0.2045835 -32.255 <2e-16 ***
## Soil_3moPreSamp 0.0143826 0.0470619 0.306 0.760
## sexFemale -0.0088614 0.0544092 -0.163 0.871
## age_dx -0.0005267 0.0028875 -0.182 0.855
## smokeHxEver -0.0376970 0.0489020 -0.771 0.441
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 1867.7 110.6 16.89 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 3619 on 6 Df
## Pseudo R-squared: 0.002781
## Number of iterations: 274 (BFGS) + 5 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -6.999856e+00 -6.197903e+00
## Soil_3moPreSamp -7.785708e-02 1.066223e-01
## sexFemale -1.155015e-01 9.777870e-02
## age_dx -6.186157e-03 5.132669e-03
## smokeHxEver -1.335433e-01 5.814920e-02
## (phi) 1.650963e+03 2.084461e+03No significant association between Soil_3moPreSamp and prop_5mC in this model.
17.8.5 Continuous Soil in 1mo Pre-Sampling
Unadjusted model
dnam_model1 <- betareg(prop_5mC ~ Soil_1moPreSamp, data=dnam)
summary(dnam_model1)##
## Call:
## betareg(formula = prop_5mC ~ Soil_1moPreSamp, data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0515 -0.3998 -0.0641 0.2723 4.9462
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.64390 0.03768 -176.345 <2e-16 ***
## Soil_1moPreSamp 0.01192 0.04470 0.267 0.79
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 1787.9 107.6 16.62 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 3499 on 3 Df
## Pseudo R-squared: 0.0003353
## Number of iterations: 1418 (BFGS) + 2 (Fisher scoring)confint(dnam_model1)## 2.5 % 97.5 %
## (Intercept) -6.71774017 -6.57005506
## Soil_1moPreSamp -0.07569086 0.09952216
## (phi) 1577.05602332 1998.76822882No significant association between Soil_1moPreSamp and prop_5mC in this model.
Adjusted model with age_dx, sex, smokeHx
dnam_model2 <- betareg(prop_5mC ~ Soil_1moPreSamp + sex + age_dx + smokeHx, data=dnam)
summary(dnam_model2)##
## Call:
## betareg(formula = prop_5mC ~ Soil_1moPreSamp + sex + age_dx + smokeHx,
## data = dnam)
##
## Standardized weighted residuals 2:
## Min 1Q Median 3Q Max
## -1.0707 -0.4040 -0.0658 0.2791 4.9473
##
## Coefficients (mean model with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.587e+00 2.096e-01 -31.423 <2e-16 ***
## Soil_1moPreSamp 1.254e-02 4.485e-02 0.280 0.780
## sexFemale 8.390e-06 5.531e-02 0.000 1.000
## age_dx -4.849e-04 2.972e-03 -0.163 0.870
## smokeHxEver -3.677e-02 5.050e-02 -0.728 0.466
##
## Phi coefficients (precision model with identity link):
## Estimate Std. Error z value Pr(>|z|)
## (phi) 1789.8 107.7 16.62 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Type of estimator: ML (maximum likelihood)
## Log-likelihood: 3500 on 6 Df
## Pseudo R-squared: 0.002581
## Number of iterations: 176 (BFGS) + 3 (Fisher scoring)confint(dnam_model2)## 2.5 % 97.5 %
## (Intercept) -6.998324e+00 -6.176552e+00
## Soil_1moPreSamp -7.536798e-02 1.004392e-01
## sexFemale -1.084026e-01 1.084194e-01
## age_dx -6.309719e-03 5.339838e-03
## smokeHxEver -1.357450e-01 6.220024e-02
## (phi) 1.578696e+03 2.000829e+03No significant association between Soil_1moPreSamp and prop_5mC in this model.
18 Impact of DNAm on Mortality
Base model
coxPH_model1 <- coxph(Surv(time_DeathTxCensor, deadORtx==1) ~ pct_5mC, data=dnam, id=ID)
summary(coxPH_model1)## Call:
## coxph(formula = Surv(time_DeathTxCensor, deadORtx == 1) ~ pct_5mC,
## data = dnam, id = ID)
##
## n= 746, number of events= 221
##
## coef exp(coef) se(coef) z Pr(>|z|)
## pct_5mC -0.1470 0.8633 0.4149 -0.354 0.723
##
## exp(coef) exp(-coef) lower .95 upper .95
## pct_5mC 0.8633 1.158 0.3828 1.947
##
## Concordance= 0.563 (se = 0.022 )
## Likelihood ratio test= 0.14 on 1 df, p=0.7
## Wald test = 0.13 on 1 df, p=0.7
## Score (logrank) test = 0.13 on 1 df, p=0.7No significant association between %5mC and mortality in this model.
Partial Model
coxPH_model2 <- coxph(Surv(time_DeathTxCensor, deadORtx==1) ~ pct_5mC + age_dx + sex + smokeHx, data=dnam, id=ID)
summary(coxPH_model2)## Call:
## coxph(formula = Surv(time_DeathTxCensor, deadORtx == 1) ~ pct_5mC +
## age_dx + sex + smokeHx, data = dnam, id = ID)
##
## n= 746, number of events= 221
##
## coef exp(coef) se(coef) z Pr(>|z|)
## pct_5mC -0.179632 0.835578 0.387670 -0.463 0.64310
## age_dx 0.024722 1.025030 0.009071 2.725 0.00642 **
## sexFemale -0.280008 0.755778 0.167263 -1.674 0.09412 .
## smokeHxEver 0.428896 1.535562 0.150184 2.856 0.00429 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## pct_5mC 0.8356 1.1968 0.3908 1.786
## age_dx 1.0250 0.9756 1.0070 1.043
## sexFemale 0.7558 1.3231 0.5445 1.049
## smokeHxEver 1.5356 0.6512 1.1440 2.061
##
## Concordance= 0.599 (se = 0.021 )
## Likelihood ratio test= 21.2 on 4 df, p=3e-04
## Wald test = 20.02 on 4 df, p=5e-04
## Score (logrank) test = 20.17 on 4 df, p=5e-04No significant association between %5mC and mortality in this model.
Complete Model
coxPH_model3 <- coxph(Surv(time_DeathTxCensor, deadORtx==1) ~ pct_5mC + age_dx + sex + smokeHx + dich_Race + fvc_pct + dlco_pct, data=dnam, id=ID)
summary(coxPH_model3)## Call:
## coxph(formula = Surv(time_DeathTxCensor, deadORtx == 1) ~ pct_5mC +
## age_dx + sex + smokeHx + dich_Race + fvc_pct + dlco_pct,
## data = dnam, id = ID)
##
## n= 637, number of events= 182
## (109 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## pct_5mC 0.283617 1.327924 0.374374 0.758 0.4487
## age_dx 0.017992 1.018155 0.010130 1.776 0.0757 .
## sexFemale -0.242890 0.784358 0.187603 -1.295 0.1954
## smokeHxEver 0.187012 1.205641 0.169357 1.104 0.2695
## dich_RaceNon-White -0.039410 0.961356 0.330653 -0.119 0.9051
## fvc_pct -0.010389 0.989665 0.005351 -1.941 0.0522 .
## dlco_pct -0.047091 0.954001 0.006616 -7.118 1.1e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## pct_5mC 1.3279 0.7531 0.6375 2.7659
## age_dx 1.0182 0.9822 0.9981 1.0386
## sexFemale 0.7844 1.2749 0.5430 1.1329
## smokeHxEver 1.2056 0.8294 0.8651 1.6803
## dich_RaceNon-White 0.9614 1.0402 0.5028 1.8380
## fvc_pct 0.9897 1.0104 0.9793 1.0001
## dlco_pct 0.9540 1.0482 0.9417 0.9665
##
## Concordance= 0.728 (se = 0.019 )
## Likelihood ratio test= 98.07 on 7 df, p=<2e-16
## Wald test = 90.24 on 7 df, p=<2e-16
## Score (logrank) test = 87.62 on 7 df, p=4e-16No significant association between %5mC and mortality in this model.
19 Impact of DNAm on Baseline FVC
Base Model
FVC_model1 <- lm(fvc_pct ~ pct_5mC, data=dnam)
summary(FVC_model1)##
## Call:
## lm(formula = fvc_pct ~ pct_5mC, data = dnam)
##
## Residuals:
## Min 1Q Median 3Q Max
## -44.491 -12.626 -0.945 11.722 52.299
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 68.2899 0.7317 93.327 <2e-16 ***
## pct_5mC -1.9395 2.9459 -0.658 0.511
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.62 on 676 degrees of freedom
## (68 observations deleted due to missingness)
## Multiple R-squared: 0.0006408, Adjusted R-squared: -0.0008375
## F-statistic: 0.4335 on 1 and 676 DF, p-value: 0.5105confint(FVC_model1)## 2.5 % 97.5 %
## (Intercept) 66.85321 69.726671
## pct_5mC -7.72373 3.844698No significant association between %5mC and baseline FVC.
Partial Model
FVC_model2 <- lm(fvc_pct ~ pct_5mC + sex + age_dx, data=dnam)
summary(FVC_model2)##
## Call:
## lm(formula = fvc_pct ~ pct_5mC + sex + age_dx, data = dnam)
##
## Residuals:
## Min 1Q Median 3Q Max
## -41.727 -11.494 -0.959 11.328 50.134
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 43.11370 5.53407 7.791 2.53e-14 ***
## pct_5mC -1.35251 2.90813 -0.465 0.642
## sexFemale 1.79770 1.45654 1.234 0.218
## age_dx 0.35917 0.07903 4.545 6.52e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.39 on 674 degrees of freedom
## (68 observations deleted due to missingness)
## Multiple R-squared: 0.03124, Adjusted R-squared: 0.02693
## F-statistic: 7.245 on 3 and 674 DF, p-value: 8.664e-05confint(FVC_model2)## 2.5 % 97.5 %
## (Intercept) 32.247610 53.9797940
## pct_5mC -7.062587 4.3575710
## sexFemale -1.062205 4.6576114
## age_dx 0.203992 0.5143502No significant association between %5mC and baseline FVC.
Complete Model
FVC_model3 <- lm(fvc_pct ~ pct_5mC + sex + age_dx + dich_Race + smokeHx, data=dnam)
summary(FVC_model3)##
## Call:
## lm(formula = fvc_pct ~ pct_5mC + sex + age_dx + dich_Race + smokeHx,
## data = dnam)
##
## Residuals:
## Min 1Q Median 3Q Max
## -41.127 -11.729 -0.653 11.433 49.479
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 42.12376 5.59302 7.531 1.62e-13 ***
## pct_5mC -1.42849 2.90506 -0.492 0.623
## sexFemale 1.98176 1.46636 1.351 0.177
## age_dx 0.36560 0.07905 4.625 4.50e-06 ***
## dich_RaceNon-White -4.67740 2.84535 -1.644 0.101
## smokeHxEver 1.19281 1.31367 0.908 0.364
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.37 on 672 degrees of freedom
## (68 observations deleted due to missingness)
## Multiple R-squared: 0.03636, Adjusted R-squared: 0.02919
## F-statistic: 5.072 on 5 and 672 DF, p-value: 0.0001427confint(FVC_model3)## 2.5 % 97.5 %
## (Intercept) 31.1418685 53.1056610
## pct_5mC -7.1325846 4.2755963
## sexFemale -0.8974351 4.8609471
## age_dx 0.2103857 0.5208138
## dich_RaceNon-White -10.2642367 0.9094403
## smokeHxEver -1.3865802 3.7721931No significant association between %5mC and baseline FVC.
20 Impact of DNAm on Baseline DLCO
Base Model
DLCO_model1 <- lm(dlco_pct ~ pct_5mC, data=dnam)
summary(DLCO_model1)##
## Call:
## lm(formula = dlco_pct ~ pct_5mC, data = dnam)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.180 -10.587 -1.157 8.675 128.215
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 40.5807 0.6932 58.544 <2e-16 ***
## pct_5mC 1.2988 2.7239 0.477 0.634
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.35 on 636 degrees of freedom
## (108 observations deleted due to missingness)
## Multiple R-squared: 0.0003573, Adjusted R-squared: -0.001214
## F-statistic: 0.2273 on 1 and 636 DF, p-value: 0.6337confint(DLCO_model1)## 2.5 % 97.5 %
## (Intercept) 39.219564 41.941931
## pct_5mC -4.050202 6.647734No significant association between %5mC and baseline DLCO
Partial Model
DLCO_model2 <- lm(dlco_pct ~ pct_5mC + sex + age_dx, data=dnam)
summary(DLCO_model2)##
## Call:
## lm(formula = dlco_pct ~ pct_5mC + sex + age_dx, data = dnam)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.675 -10.335 -1.376 8.650 128.622
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 43.98484 5.36943 8.192 1.42e-15 ***
## pct_5mC 1.30348 2.72686 0.478 0.633
## sexFemale 1.62893 1.39816 1.165 0.244
## age_dx -0.05576 0.07672 -0.727 0.468
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.34 on 634 degrees of freedom
## (108 observations deleted due to missingness)
## Multiple R-squared: 0.003633, Adjusted R-squared: -0.001081
## F-statistic: 0.7707 on 3 and 634 DF, p-value: 0.5107confint(DLCO_model2)## 2.5 % 97.5 %
## (Intercept) 33.4408166 54.52886557
## pct_5mC -4.0512810 6.65824801
## sexFemale -1.1166552 4.37450659
## age_dx -0.2064242 0.09490435No significant association between %5mC and baseline DLCO
Complete Model
DLCO_model3 <- lm(dlco_pct ~ pct_5mC + sex + age_dx + dich_Race + smokeHx, data=dnam)
summary(DLCO_model3)##
## Call:
## lm(formula = dlco_pct ~ pct_5mC + sex + age_dx + dich_Race +
## smokeHx, data = dnam)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.586 -9.884 -0.975 7.828 128.148
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 47.01113 5.38189 8.735 < 2e-16 ***
## pct_5mC 1.39641 2.70181 0.517 0.605449
## sexFemale 0.95389 1.39781 0.682 0.495224
## age_dx -0.05614 0.07611 -0.738 0.461048
## dich_RaceNon-White 2.05384 2.72268 0.754 0.450923
## smokeHxEver -4.59330 1.26719 -3.625 0.000312 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.2 on 632 degrees of freedom
## (108 observations deleted due to missingness)
## Multiple R-squared: 0.02509, Adjusted R-squared: 0.01737
## F-statistic: 3.253 on 5 and 632 DF, p-value: 0.006578confint(DLCO_model3)## 2.5 % 97.5 %
## (Intercept) 36.4425873 57.57968163
## pct_5mC -3.9092120 6.70202324
## sexFemale -1.7910254 3.69881159
## age_dx -0.2055931 0.09332124
## dich_RaceNon-White -3.2927491 7.40042565
## smokeHxEver -7.0817190 -2.10488804No significant association between %5mC and baseline DLCO.